The environmental case for RAP is compelling. Reclaimed asphalt pavement is generated in enormous quantities as infrastructure is built and renewed, and recycling it into new pavement alleviates waste pressure and supports green, low-carbon development. Yet the mechanical behavior of recycled mixtures is complicated by long-term service factors such as aggregate damage from loading and increased brittleness of aged asphalt. Cracking resistance deteriorates as RAP content rises, which has restricted high-content applications. Prior studies have shown that when RAP content exceeds 30 percent, fatigue performance decreases due to binder hardening, but the underlying influence mechanism has remained incompletely understood.

Laboratory tests alone cannot fully track the evolution from microcrack initiation to specimen failure. Techniques such as CT scanning and image analysis have limitations in following crack development in real time and in quantifying how much each mesoscopic component contributes to cracking. The discrete element method fills this gap by enabling investigation of stress characteristics, damage initiation, and crack propagation throughout the entire loading process at the mesoscopic scale. Over the past decade, researchers have applied DEM to recycled asphalt mixtures, analyzing meso-mechanical responses through indirect tensile tests and confirming that bonding parameters and RAP morphology significantly influence virtual splitting results. However, existing models have not adequately characterized the aged asphalt on RAP surfaces and its non-uniform distribution, which limits the fidelity of mesostructure simulation.

To address this, the team constructed DEM models of splitting specimens with a diameter of 101.6 millimeters using PFC5.0 2D software. Eight different aggregate contours obtained from scanning real aggregates served as the basis for generating coarse aggregate Clump models, since aggregate morphology critically influences mechanical properties. Fine aggregates smaller than 2.36 millimeters were represented by circular particles of 0.2 to 0.3 millimeters treated as asphalt mortar filler to improve computational efficiency. The modeling proceeded through six steps, from particle filling and specimen space definition, through servo compaction forming a three-phase structure of coarse aggregate, asphalt mastic, and voids, to locking aggregate contours into crushable Clumps grouped with the parallel bond model.

A distinctive feature of the models is their treatment of aged asphalt. Particles in direct contact with the RAP surface were identified through a traversal search and classified as the aged asphalt component. Because different aggregate contours can produce offset overlapping contact, the aged asphalt thickness ranged from 0.2 to 0.6 millimeters. Coating particle layers were generated on RAP surfaces, identified in yellow, and assigned mesoscopic parameters different from those of new asphalt, with reduced bond toughness and increased brittleness. The bond strength of the interfacial transition zone was set lower than that of the asphalt binder itself. Mesoscopic parameters were calibrated against indoor splitting test results, with the criterion that errors in maximum load and splitting strength remain below 10 percent, which the final models achieved.

The laboratory splitting tests were conducted on standard Marshall-compacted specimens 101.6 millimeters in diameter and 63.5 millimeters high, at 15 degrees Celsius and a loading rate of 50 millimeters per minute, with three replicate tests per RAP content. Both splitting strength and maximum load decreased progressively with increasing RAP content in a nonlinear pattern. When RAP rose from 15 to 30 percent, the maximum load declined only slightly, but at 45 percent the rate of decline accelerated markedly. At 60 percent RAP, the maximum load was reduced by 46.4 percent compared with the 15 percent mixture, and splitting strength followed a similar trend, indicating particularly pronounced deterioration in tensile performance beyond 45 percent RAP.

To monitor stress evolution, the researchers divided the DEM model into eight equal-area regions and placed measurement circles of 12.5 millimeter radius at their centers. Taking the 30 percent RAP mixture as a representative case, all measurement points exhibited negative stress responses, confirming the specimen was in overall compression. Measurement circles #1 through #4, directly subjected to loading along the vertical axis, showed sharp stress drops with peak values between −0.5 and −3.5 megapascals, where weak interfacial bonds between RAP and aggregates produced stress concentration and microcrack initiation. Circles #5 through #8 on both sides experienced lower stress levels and acted as buffer zones. By the later stage, microcrack development in the loading-axis region caused severe damage while the lateral curves stabilized, showing that the deep negative stress zone preferentially becomes the site of microcrack propagation and local failure. Shear stress peaked near 1.1 megapascals before declining toward zero at failure.

Crack morphology changed dramatically with RAP content. At 15 percent, cracks were dominated by a single primary crack with a concentrated, straight path along the loading direction, mainly near interfaces between aged and new materials. At 30 percent, the primary crack remained dominant but locally detoured around coarse aggregates before returning to the main path. At 45 percent, the primary crack was accompanied by multiple secondary cracks with obvious deflection and bifurcation, and the interface became the main failure path. At 60 percent, the primary crack branched into multiple crack bands with repeated deflections and convergences forming a network-like pattern. Tensile microcracks accounted for more than 97 percent of the total at all RAP contents, demonstrating that splitting failure is essentially tensile failure induced when transverse tensile stress exceeds the material limit, while shear contributes little to damage evolution. The total number of microcracks increased by 16 percent when RAP content rose beyond 30 percent to 45 and 60 percent.

Aggregate breakage also displayed concentration and directionality. Breakage was mainly distributed in the two end regions along the loading direction, accounting for 70 percent of the total, with the highest degree in measurement circles #1 and #4. Breakage in the lateral circles depended on whether crack paths traversed them. The total breakage index ranged from 4 to 10 percent and exhibited an exponential positive correlation with RAP content. RAP aggregates may have already developed microcracks during their service life, reducing inherent strength, so sustained principal tensile stress concentration at the loading-axis ends promoted initiation, propagation, and coalescence of microcracks in weak interfacial bonding zones, eventually forming primary cracks.

Correlating mesoscopic damage indicators with macroscopic mechanical responses revealed clear negative relationships. As RAP content increased, the total number of microcracks grew while maximum load and splitting strength declined, because continuous microcrack propagation reduced the effective load-bearing cross-section. The total breakage index likewise correlated negatively with splitting strength, since aggregate breakage restructures the force chain network and weakens the skeleton’s load transfer capacity. Together, the microcrack count characterizes damage to bonding performance while the breakage index characterizes skeleton degradation, and mesoscopic damage ultimately degrades macroscopic performance through two pathways: microcrack coalescence and skeleton weakening. The authors note limitations, including reliance on a specific gradation and a 2D approach, and plan future work with multiple gradations, various rock types, 3D DEM models, and measurement circles analyzing contact force variation in RAP-concentrated and virgin aggregate zones. The findings provide a theoretical basis for design optimization of recycled asphalt mixtures.

Subject of Research: Mesoscopic crack evolution and cracking resistance of recycled asphalt mixtures with different RAP contents simulated using the discrete element method

Article Title: Mesoscopic crack evolution and cracking resistance of recycled asphalt mixtures with different RAP contents using discrete element method

Article References: Li, H., Zhang, H., Huang, Y., Liu, Q., Zhan, J., & Lin, Y. (2026). Mesoscopic crack evolution and cracking resistance of recycled asphalt mixtures with different RAP contents using discrete element method. Frontiers in Materials, 13, Article 1967974. https://doi.org/10.3389/fmats.2026.1967974

Image Credits: AI Generated

DOI: 10.3389/fmats.2026.1967974

Keywords: recycled asphalt pavement, RAP content, discrete element method, cracking resistance, microcrack evolution, splitting test, aged asphalt, aggregate breakage, mesoscopic damage, asphalt mixture, crack propagation, tensile failure

The heart of the problem is a mismatch of scales. The Discrete Element Method, or DEM, has become the workhorse of computational granular physics since its development by Peter Cundall in 1971 and its landmark extension with Otto Strack in 1979. DEM applies Newton’s second law of motion to the centre of every particle and a force-displacement law at every contact, computing the trajectory of each grain through time. Collisions are typically modelled as viscoelastic spring-dashpot systems, capturing both the repulsive force and the dissipated kinetic energy. Coupled with Computational Fluid Dynamics in the early 1990s, DEM-CFD can now simulate landslides, debris flows, pyroclastic currents, riverbed transport and industrial powder handling with remarkable fidelity. But what the simulations deliver is a torrent of particle-scale data: individual velocities, individual forces, individual contacts. Science and engineering, however, speak the language of continuum fields such as pressure, stress, strain rate and density.

Bridging that gap requires a mathematical procedure known as coarse-graining, a discrete-to-continuum transformation in which macroscopic fields emerge as spatially weighted averages of microscopic quantities. Each particle contributes to the field at a given point according to a weighting function that spreads its influence over a small volume, effectively smearing the point-like mass of the particle into a smooth density. Crucially, the method makes no assumption that particles are spherical or rigid, and it works across flow regimes from solid-like quasi-static behaviour to fluid-like rapid flow. The theoretical foundations trace back to Babic’s 1997 averaging framework for granular media, itself inspired by spatio-temporal weighted averaging in molecular dynamics, and have been refined over decades to handle boundaries, scale dependence and polydisperse mixtures.

Despite this maturity, the software ecosystem has lagged. Some DEM packages, such as LAMMPS and MercuryDPM, include built-in coarse-graining capabilities. Others, notably the open-source MFiX-DEM developed by the United States National Energy Technology Laboratory, do not, forcing users to write custom post-processing code or contort their simulation outputs to fit other tools. That fragmentation, the Edinburgh team argues, hampers reproducibility and invites error propagation. Pysammos, whose name nods to Archimedes’ The Sand Reckoner and his attempt to count the grains of sand that could fill the universe, was built to fill this void: a user-friendly, computationally efficient Python package that reads MFiX-DEM output directly and produces continuum fields ready for analysis and visualisation.

Technically, the package implements the full weighted-average machinery. It computes the mass density, momentum density and velocity fields, and it evaluates the stress tensor as the sum of a contact part and a kinetic part. Contact forces are distributed along the branch vector connecting two touching particles through a line integral of the weighting function, which Pysammos evaluates numerically with a trapezoidal rule that the authors show is already converged with just ten sampling points. Users can choose among three smoothing kernels: the Lucy polynomial, a cut-off Gaussian and a Heaviside step function. The authors recommend the Lucy function, because the Heaviside kernel weights all particles in the averaging volume equally and thereby amplifies edge effects, while the truncated Gaussian technically violates the differentiability required for the continuum balance equations to hold exactly.

The choice of smoothing width, the resolution at which the continuum fields are computed, turns out to be anything but trivial. The kinetic stress tensor is intrinsically dependent on the square of the averaging width, because it captures velocity gradients between a particle’s position and the evaluation point. Previous work has identified two length scales at which coarse-grained fields become nearly independent of the averaging width: one below the particle scale, which can resolve thin flow layers near boundaries, and one at the particle scale, which yields smooth fields. Pysammos adopts a default smoothing width of 0.75 times a representative particle diameter, but the team stresses that users should verify the stability of their results for their particular flow regime.

One of the package’s most distinctive features is its automated phase detection. In polydisperse mixtures, particles segregate by size and density through competing mechanisms known as kinetic sieving and buoyancy. Pysammos clusters the particle data by diameter and density using the k-means algorithm, selecting the optimal number of phases via silhouette analysis, and then computes partial continuum fields for each phase separately. Mixture theory then superposes these partial fields to recover the bulk behaviour. The team demonstrated this on a simulated granular pile confined between walls, revealing the classic stress-arching phenomenon: instead of the hydrostatic pressure maximum a fluid would show beneath the pile’s peak, the basal pressure exhibits a local minimum because force chains redirect the weight sideways toward the flanks.

The showcase applications span the geosciences. A bedload transport simulation of polydisperse, non-spherical grains under travelling water waves revealed bands of elevated shear rate and inertial number at the wave fronts, invisible in the raw contact network. A high-speed impact on a million-particle granular bed, relevant to cratering on asteroids and seismology alike, showed a downward-propagating shock wave and a friction coefficient exceeding the static value around the impact zone. A pseudo-2D simulation of crystals suspended in magma flowing through a conduit quantified how the effective shear viscosity varies with wall friction. And an erodible-bed experiment reproduced the classic Bagnold velocity profile, with granular temperature increasing toward the base where grains rattle against a rough floor.

Benchmarking against established coarse-graining software, including MercuryCG, EDEM’s continuum analysis, Granulysed and Iota-Suite, showed that Pysammos reproduces the expected fields, with results from its Gaussian and Lucy kernels coinciding closely with MercuryCG’s weighted averaging scheme. Performance tests on the ARCHER2 supercomputer showed that the cost per particle decreases sub-linearly with system size, meaning overheads are amortised and caches are used efficiently. Efficient execution does not demand massive parallel resources: at sub-particle resolution a couple of cores suffice, while large dense packings at coarser resolution benefit from four to sixteen cores. That makes the tool practical on ordinary desktop machines as well as high-performance clusters, lowering the barrier for research groups without dedicated computing infrastructure.

The team positions Pysammos as a contribution to a broader effort to standardise and streamline DEM post-processing across an inherently interdisciplinary community spanning geosciences, engineering and physics. Future versions are planned to read data from other DEM packages such as LAMMPS and YADE, to offer more flexible meshing beyond the current structured cuboid grid, and to account for the effect of hard boundaries on the contact stress tensor. Work is also under way with the MFiX developers to enable rigorous coarse-graining of glued-sphere particles, which approximate irregular shapes such as angular volcanic ash. For a field in which the second-most-handled material by weight in global industry, behind only water, still defies a unified description, a well-documented, open-source bridge from grain to continuum may prove a quietly transformative piece of infrastructure.

Subject of Research: A discrete-to-continuum coarse-graining software tool for analysing the rheology of granular materials from DEM simulations

Article Title: Pysammos 1.0.0: a discrete-to-continuum transformation Python tool to analyse the rheology of granular materials

Article References: Pysammos 1.0.0: a discrete-to-continuum transformation Python tool to analyse the rheology of granular materials. (n.d.). https://doi.org/10.5194/gmd-19-9519-2026

Image Credits: AI Generated

DOI: 10.5194/gmd-19-9519-2026

Keywords: granular flows, Pysammos, Discrete Element Method, coarse-graining, rheology, DEM-CFD, MFiX, geohazards, sediment transport, open-source software, computational physics, continuum mechanics

The research, published in the journal Plant Methods, tackles a deceptively simple question: how do you make irregularly shaped objects like maize ears line up and flow smoothly along a vibrating conveyor? Unlike spheres or cubes, maize ears tumble, rotate, and jam unpredictably. Traditional response surface methodology, the statistical workhorse engineers typically use to tune machinery parameters, simply cannot capture the strongly nonlinear behavior of these tumbling biological objects. The team, led by co-first authors Yingjie Li and Yalei Xu, recognized that a fundamentally new approach was needed.

Their solution was a three-part framework they describe as mechanism simulation, intelligent modeling, and global optimization. The first step involved building a discrete element model, a computational technique that treats each maize ear as an individual particle with realistic physical properties, allowing researchers to simulate exactly how thousands of ears would behave under different vibration conditions. This digital twin of the conveying process gave them a window into physics that would be nearly impossible to observe directly at speed.

To train their models, the researchers needed high-quality data, and lots of it. They employed a hybrid sampling strategy that combined Box-Behnken designs with Latin hypercube sampling, generating 337 sets of high-fidelity simulation data. This clever pairing ensured that the data covered both the systematic experimental design points and the broad parameter space needed for machine learning. When they validated their simulations against physical bench experiments across multiple operating conditions, the average relative error came in at less than five percent, a remarkably tight agreement for a system involving chaotic biological materials.

With the dataset in hand, the team built three competing predictive models: the traditional response surface methodology, a neural network architecture called an extreme learning machine, and an enhanced version of that network tuned by a grey wolf optimizer. The grey wolf optimizer is a nature-inspired algorithm that mimics the leadership hierarchy and hunting strategy of wolf packs to search parameter space efficiently. Five-fold cross-validation revealed a decisive winner: the grey wolf-optimized extreme learning machine achieved a coefficient of determination exceeding 0.98, dramatically outperforming the classical statistical approach on this stubbornly nonlinear problem.

The optimized model then pointed the way to an ideal operating regime: a vibration frequency of 16 hertz, an amplitude of 6 millimeters, and a vibration direction angle of 30 degrees. Under these conditions, the model predicted a feeding rate of 0.9981 ears per second, meaning the conveyor would deliver essentially one ear every second without interruption. The coefficient of variation, a measure of flow uniformity, was predicted at 43.59 percent. When the team put these parameters to the test on a physical three-level bench system, reality matched prediction almost exactly, with a measured feeding rate of 1.0056 ears per second and a coefficient of variation of 43.92 percent.

Perhaps most striking was the comparison with the conventional approach. Compared with the optimization results from response surface methodology alone, the new method increased the feeding rate by 41.6 percent while cutting the coefficient of variation by 29.6 percent. In practical terms, that means more ears flowing through the system per second, arriving in a steadier, more predictable stream, and with far less of the jostling and impact that causes mechanical damage to seed samples.

Beyond the headline numbers, the study delivered something arguably more valuable: a mechanistic understanding of why certain vibration settings work better than others. The researchers found that vibration frequency is the dominant factor controlling how quickly maize ears converge to an ordered posture, and therefore how fast the system can feed. Amplitude and vibration direction angle, by contrast, act as a coupled pair that governs the smoothness of posture adjustment, and thus the stability of the conveying process. This division of labor gives engineers a clear design principle: tune frequency for speed, then balance amplitude and angle for stability.

The implications extend well beyond maize. The framework of mechanism simulation, intelligent modeling, and global optimization is explicitly designed to be transferable to other irregularly shaped seeds and biological materials that resist conventional handling. In plant breeding programs, where seed samples are irreplaceable and mechanical damage can destroy years of genetic work, the ability to convey materials gently and in an orderly fashion directly protects research value. The method could also inform the design of seed counters, singulators, and precision planters that depend on reliable single-file feeding.

The work, supported by the Xinjiang Uygur Autonomous Region High-end Intelligent Agricultural Machinery Industry Innovation Research Institute, represents a growing trend in agricultural engineering: replacing trial-and-error tuning with simulation-driven, machine-learning-accelerated design. As farms and breeding facilities demand ever-faster, gentler handling of biological materials, approaches like this one, which marry the physics of granular flow with the pattern-finding power of modern algorithms, are likely to become the standard rather than the exception. For the humble maize ear, it means a smoother ride from field to laboratory, and for the scientists who depend on those ears, it means better data with less loss.

Subject of Research: Ordered vibration conveying mechanism and parameter optimization for maize ears in plant breeding

Article Title: Mechanism and parameter optimization method for ordered vibration conveying of maize ears

Article References: Li, Y., Xu, Y., Li, J., Li, Y., & Wang, X. (2026). Mechanism and parameter optimization method for ordered vibration conveying of maize ears. Plant Methods. https://doi.org/10.1186/s13007-026-01580-z

Image Credits: AI Generated

DOI: 10.1186/s13007-026-01580-z

Keywords: maize ears, vibration conveying, discrete element method, extreme learning machine, grey wolf optimizer, parameter optimization, plant breeding, seed handling, agricultural machinery, surrogate modeling, response surface methodology, ordered conveying

The core of the proposal is a dual-parameter damage index built from two quantities: the Area Loss Ratio, abbreviated ALR, and the Residual Height Ratio, or RHR. The ALR describes the fraction of the tower’s horizontal cross-sectional area that has been lost as blocks detach and fall away, while the RHR captures how much of the original height remains standing after partial collapse. Together, the two parameters trace a damage trajectory that a single measure cannot. A tower might lose a modest amount of area near its crown while retaining most of its height, or it might shear through at mid-height and shed an entire upper story; the combination of ALR and RHR distinguishes these very different failure modes and allows them to be ranked on a common severity scale.

To calibrate the index, the researchers modeled seven representative masonry pagodas in three dimensions using the discrete element method, a computational technique originally developed for rock mechanics. Unlike continuum finite element models, which treat masonry as a smeared material, the discrete element approach represents the structure as an assembly of individual polyhedral blocks interacting through contacts. That matters for pagodas, because their failure under earthquake shaking is fundamentally a process of blocks cracking loose, sliding, rotating and toppling. The formulation traces back to the distinct element schemes introduced by Peter Cundall and collaborators in the late 1980s, and it allows the simulation to capture progressive detachment and collapse rather than merely the onset of cracking.

Within the numerical models, the team systematically varied the key structural parameters that govern pagoda behavior. Three stood out: the condition of the internal infill, the height-to-width ratio of the tower, and the opening ratio, meaning the proportion of the facade occupied by doorways, windows and arched niches. Each of these features varies widely across the surviving stock of ancient pagodas, and each plausibly changes how the structure responds to shaking. By building models that span this parameter space, the researchers could isolate the influence of each factor on displacement demand and collapse susceptibility, rather than studying a single iconic tower in isolation.

The nonlinear dynamic analyses subjected the seven pagoda models to multiple ground motion records at two hazard levels drawn from Chinese seismic design practice: a frequent earthquake with a peak ground acceleration of 0.07 g, and a rare earthquake with a peak ground acceleration of 0.40 g. The gap between those two levels is enormous, roughly a factor of nearly six in acceleration, and the simulations show how dramatically the damage picture changes across it. Under frequent shaking, the towers largely remain intact, with cracking confined to vulnerable zones around openings. Under rare shaking, the response escalates into block detachment and partial collapse, particularly for the most vulnerable configurations.

The parameter study yielded clear and intuitive trends. Internal infill, the material packed inside the hollow shell of many pagodas, significantly enhances global stiffness and reduces seismic responses, effectively bracing the tower from within. Conversely, increasing the height-to-width ratio amplifies displacement demand at the top of the structure, and larger opening ratios weaken the load-bearing walls and concentrate stress around voids. The most striking results came from hollow-core towers and fully opened configurations under rare earthquakes: these models exhibited pronounced block detachment and partial collapse, with area loss ratios reaching 10 to 20 percent and residual height ratios dropping to approximately 80 percent. In other words, a fifth of the tower’s footprint could be lost and its standing height reduced to four-fifths of the original before the process stabilized.

From the evolution of ALR and RHR across the simulation suite, the team distilled a four-level collapse classification framework that spans the entire damage process, from initial cracking through to global collapse. The lowest level corresponds to the appearance of cracks without meaningful geometric loss, while the intermediate levels capture progressive block detachment and partial sectional failure. The highest level denotes global collapse, the point at which the residual structure can no longer carry its own weight. Because the classification is anchored to measurable geometry rather than subjective inspection categories, two assessors looking at the same damaged tower should, in principle, arrive at the same severity rating, which is precisely what post-earthquake triage requires.

A crucial test for any damage index is whether it works in the messy reality after a real earthquake, when survey teams may have only photographs and rough sketches rather than laser scans. The researchers validated their method against damage observed in masonry pagodas during the 2008 Wenchuan earthquake, the catastrophic event in Sichuan Province that damaged thousands of heritage structures. The comparison demonstrated that the proposed index can reliably quantify collapse severity even when detailed geometric loss data are unavailable, which is a decisive practical advantage. Emergency responders and heritage authorities rarely have the luxury of complete documentation in the days after a major quake, so an index that tolerates incomplete data is far more useful in the field than one that demands it.

The significance of the work extends beyond China’s borders. Masonry towers and pagodas are a global heritage category, from Italian campaniles to Nepalese temple towers, and seismic risk assessments for such structures have proliferated in recent years, using tools ranging from ambient vibration monitoring and Bayesian model updating to limit analysis and shaking table tests. What most of these methods share is a focus on predicting damage before it happens or detecting it through dynamic signatures. The new dual-parameter index addresses a different and complementary question: once damage has occurred, how bad is it, quantitatively? That question drives triage decisions, such as which towers need immediate shoring, which can await scheduled restoration, and which are safe for the public to approach.

The authors suggest the method can serve as a robust and practical tool for post-earthquake damage assessment and for decision making in the conservation and seismic risk mitigation of historic masonry structures. Because ALR and RHR are geometric ratios, they can in principle be estimated from visual inspection, photogrammetry or laser scanning, and they translate directly into the language conservators use when planning interventions. The research was supported by the National Natural Science Foundation of China, the Key Research and Development Program of Shaanxi Province and a doctoral dissertation innovation fund from Xi’an University of Technology. For the custodians of ancient towers standing in one of the world’s most seismically active regions, the study offers something rare: a damage scale designed for their buildings, validated against a real catastrophe, and simple enough to be used when it matters most, in the chaotic hours and days after the ground stops shaking.

Subject of Research: A dual-parameter geometric damage index for quantifying seismic collapse severity in historic masonry pagodas using discrete element modeling

Article Title: A novel dual-parameter method for evaluating seismic collapse severity in masonry pagodas

Article References: Li, M., Lu, J., Wang, D., Jia, X., Wu, X., Mao, D., Yun, Z., & Wang, Z. (2026). A novel dual-parameter method for evaluating seismic collapse severity in masonry pagodas. Bulletin of Earthquake Engineering. https://doi.org/10.1007/s10518-026-02661-z

Image Credits: AI Generated

DOI: 10.1007/s10518-026-02661-z

Keywords: masonry pagodas, seismic collapse, damage index, discrete element method, area loss ratio, residual height ratio, earthquake engineering, cultural heritage, Wenchuan earthquake, structural health assessment, nonlinear dynamic analysis, post-earthquake assessment

The research team, led by Hangli Yang and Haoxiang Luan with colleagues including Fei Geng, Yupeng Xu, Hongke Zheng, and Jianguang Xie, built a three-dimensional discrete element model, or DEM, of macroporous recycled concrete and constrained it with an unusually rich set of laboratory measurements. Unlike continuum models that treat concrete as a smeared material, DEM represents the structure as thousands of individual particles connected by bonds that can stretch, shear, and snap. That makes it possible to watch the internal load-bearing network, known as force chains, reorganize as frost damage accumulates. Because no existing simulation platform can directly solve heat transfer, ice–water phase change, and moisture migration together, the team used an experimentally calibrated equivalent-damage approach, updating contact stiffness and bond strengths cycle by cycle rather than applying a single blanket reduction.

The experimental foundation came from the group’s earlier tests on three concrete mixes, labeled M1–20%, M2–25%, and M5–30%, where the numbers denote paste type and designed porosity. The recycled coarse aggregate, crushed from demolished concrete with particles between 9.5 and 37.5 millimeters, consisted of a natural stone core wrapped in a layer of old cement paste covering 23.1 percent of its surface. The researchers measured an impressive array of properties at different numbers of freeze–thaw cycles: compressive, splitting tensile, and shear strengths of the new paste; nanoindentation elastic moduli of the paste, the old paste, and their interfaces with natural aggregate; the rate at which old paste detached from recycled aggregate; and the mass change, strength, and elastic modulus of full specimens.

One of the most striking findings emerged immediately. The specimens did not simply decay monotonically. Instead, their mass and elastic modulus both rose during the first 40 cycles before declining. The explanation lies in a chemical race: water entering the connected pores promoted continued hydration of residual unhydrated cement, and the additional hydration products partially filled small pores, stiffening the paste and its interfaces faster than early frost microdamage could soften them. Only after about 40 cycles did accumulated microcracking, interfacial debonding, and surface spalling win out, sending stiffness into a steep decline. Freeze–thaw deterioration, the study shows, is a staged process of competing mechanisms rather than a steady slide.

To capture the geometric side of damage, the team converted measured mass loss into a count of lost surface paste particles. Because spalling occurred mainly in new-paste bridge regions rather than in aggregate cores, the net solid mass loss could be mapped onto removal of surface paste elements from the model. The algorithm allowed only particles on the current external surface, identified by a bond coordination number below six, to be deleted, so erosion progressed gradually inward without creating nonphysical internal voids. By 160 cycles, the models had shed 817 particles for the densest mix and up to 1,775 for the most porous one, each removal representing a real loss of load-bearing contact.

Validation against compression tests was convincing. Mean errors in peak stress across all groups and cycle counts ranged from 5.2 to 6.0 percent, while peak-strain errors stayed between 12.0 and 15.5 percent. The simulated elastic moduli, though noisier with errors of 10.8 to 18.1 percent, reproduced the characteristic rise-and-fall trend, and a new porosity-controlled prediction equation for post-freeze–thaw elastic modulus achieved a mean calculation error of just 7.6 percent. After 160 cycles, the peak-stress losses reached 38.3 percent for M2–25%, 35.8 percent for M5–30%, and 32.6 percent for M1–20%, matching the laboratory ordering and confirming that freeze–thaw resistance is governed jointly by porosity and the quality of the paste and its interfaces.

The deepest insight came from tracking force chains, the quasi-linear chains of highly stressed contacts that carry most of the load through granular skeletons. In undamaged and early-cycle specimens, load flowed through a few dominant strong chains concentrated in the specimen center and along inclined diagonal paths, producing the classic X-shaped shear failure. The coefficient of variation of force-chain intensity peaked at 1.847 at 40 cycles, when stress concentration was greatest. As cycling continued, however, the degradation of paste-related interfaces and the loss of surface particles progressively severed these dominant chains, forcing load to redistribute from a few high-capacity paths to many dispersed low-capacity ones. The coefficient of variation fell to 1.609 by 160 cycles.

That decline might superficially look like homogenization, as if the material were becoming more uniform and therefore tougher. The researchers show it is anything but. They call the phenomenon damage-induced pseudo-homogenization: the apparent evening-out of the force-chain distribution arises purely because the strongest load paths have failed, at the very moment peak stress and elastic modulus continue to fall. Simultaneously, the proportion of tensile bond failures grew, rising by 4.3 to 6.3 percent across the groups after 160 cycles, and the macroscopic failure mode shifted from X-shaped shear to longitudinal splitting as cracks found weakened interfaces and coalesced parallel to the loading direction. The toughness index dropped by up to 33.1 percent in the most porous mix, meaning the extra deformation capacity reflected damage, not ductility.

The bond-failure statistics also pinpointed the material’s Achilles’ heel. Across all freeze–thaw states, the failure rate ordering never changed: the interfaces between new and old paste, and between new paste and natural aggregate, failed most often, followed by paste-to-paste bonds, with old-paste-related bonds failing least. After 160 cycles, the fastest-growing failure rates belonged precisely to those new-paste interfaces, identifying them as the weak zones through which frost damage first develops and then transfers into the load-bearing skeleton. Notably, the tensile-failure proportion began climbing after 120 cycles for the densest mix but after only 40 cycles for the most porous one, showing how higher porosity concentrates stress and accelerates the onset of terminal damage.

Beyond its scientific contribution, the work carries practical weight for cold-region infrastructure. Permeable pavements, municipal and airport drainage bases, and sponge-city surfaces built with recycled aggregate can now be evaluated with a model that distinguishes which phases and interfaces are degrading, rather than relying on bulk indicators like mass loss or dynamic modulus that blur the picture. The authors suggest their framework can guide material selection, pore-structure design, durability assessment, and maintenance planning. The study’s validation remains an internal consistency check within a single material system, so independent confirmation on other mixes and laboratories will be the next step. But the central message stands: when ice attacks porous recycled concrete, the most dangerous change is not the damage you can weigh, but the silent rewiring of the force chains that hold the pavement together.

Subject of Research: Mesoscopic discrete element modeling of freeze–thaw deterioration mechanisms in macroporous recycled concrete

Article Title: Mesoscopic DEM study of freeze–thaw deterioration in macroporous recycled concrete: Interfacial damage, particle spalling, and force-chain reconstruction

Article References: Mesoscopic DEM study of freeze–thaw deterioration in macroporous recycled concrete: Interfacial damage, particle spalling, and force-chain reconstruction. (n.d.). https://doi.org/10.1016/j.cscm.2026.e06561

Image Credits: AI Generated

DOI: 10.1016/j.cscm.2026.e06561

Keywords: macroporous recycled concrete, freeze–thaw cycles, discrete element method, force chains, interfacial transition zone, recycled aggregate, surface spalling, pseudo-homogenization, permeable pavement, sponge city, elastic modulus, bond failure

The research team, led by Haitao Ge of Chang’an University together with collaborators in France, built their framework around a deceptively simple idea: pavement texture is not an abstract surface property but the direct geometric fingerprint of how individual aggregate particles pack together near the top of an asphalt layer. If you can digitally reproduce the shape, size distribution, and arrangement of those particles, and then account for the smoothing role of the asphalt binder that glues them together, you should be able to predict the surface that a paving machine will produce. The challenge was doing so with enough physical realism to match what laser scanners actually measure on real roads.

To establish a trustworthy benchmark, the researchers first scanned real pavement surfaces using a high-resolution three-dimensional laser scanner, the Freescan UE11, which operates by laser triangulation and achieves a measurement accuracy of 0.02 millimeters while capturing up to 1,020,000 points per second. From the dense point clouds, they extracted representative 90 by 90 millimeter regions and processed them through noise filtering, outlier removal, and plane detrending, fitting and subtracting a least-squares plane so that only texture-related roughness remained. The cleaned data were interpolated onto a regular grid with a fine 0.1 millimeter resolution, producing continuous height fields suitable for rigorous statistical analysis.

Two widely used asphalt mixtures served as the test cases: a dense-graded AC-13 and a stone matrix asphalt SMA-13. The measured surfaces revealed clear and quantifiable differences. SMA-13 showed consistently higher amplitude parameters, with its mean profile depth, a standard macrotexture indicator, exceeding that of AC-13 by roughly 47 percent. Both mixtures displayed negative skewness, indicating valley-dominated surfaces, but SMA-13’s less negative skewness and lower kurtosis revealed a more balanced distribution of peaks and valleys created by large protruding aggregates. In contrast, AC-13 presented a smoother, more continuous surface with sharper, more concentrated asperities, reflecting its denser and more uniformly packed gradation.

The heart of the digital reconstruction lies in how the aggregates themselves were represented. The team laser-scanned approximately 500 real aggregate particles with sieve sizes from 4.75 to 26.5 millimeters, converting each into a triangular surface mesh. From these scans they quantified elongation, flatness, and sphericity, finding that all three descriptors followed normal distributions with high goodness of fit, which allowed them to stochastically generate a particle library of realistic polyhedral shapes. Because full-resolution meshes would be computationally prohibitive, they systematically simplified the geometry, showing that reducing particles to about 60 to 65 faces preserved morphological indices within deviations of roughly 4 percent while dramatically cutting simulation cost.

For the simulation engine, the researchers chose the Contact Dynamics method rather than classical discrete element approaches. Conventional discrete element models rely on stiff elastic contact laws that demand artificially high stiffness parameters and tiny time steps, often causing numerical instability when irregular polyhedral particles collide. Contact Dynamics instead enforces impenetrability through non-smooth mechanics, using the Signorini condition for normal contact and the Coulomb friction law for tangential interaction, solved implicitly with a nonlinear Gauss-Seidel algorithm in the LMGC90 software. This formulation handles point, edge-face, and face-face contacts between angular particles without artificial stiffness, permitting larger time steps and mechanically consistent simulations of large assemblies of irregular grains.

The virtual specimens were assembled by inserting particles according to the target gradation curves of each mixture, with particles finer than 2.36 millimeters lumped into an implicit mastic phase to save computation. Gravitational deposition created a loose configuration in a 100 by 100 millimeter domain with periodic horizontal boundaries, mimicking an infinitely repeating material volume. Compaction then proceeded under a constant vertical force of 6 kilonewtons, equivalent to about 0.6 megapascals of compressive stress, superimposed with a 40-hertz harmonic vibration representative of real vibratory rollers. A reduced inter-particle friction coefficient of 0.1 accounted for the lubricating effect of hot bitumen, and compaction continued until mechanical stabilization at a specimen thickness of roughly 40 millimeters.

When the researchers extracted surface textures from these compacted digital specimens using a virtual scanning procedure that mimics laser triangulation, the raw results revealed a fundamental problem. The purely aggregate-controlled surfaces overestimated roughness dramatically, with mean profile depth nearly doubled for both mixtures, because the digital surface lacked the binder mastic that in reality fills deep valleys and bridges neighboring stones. To correct this, the team introduced a bridging algorithm applied directly to the height field: a maximum filter that locally elevates narrow valleys, a grayscale morphological closing operation that removes small isolated depressions, and optional Gaussian smoothing. Calibrated once, the same parameters worked for both mixtures, cutting the deviation in mean profile depth from 160.9 percent to 27.4 percent for AC-13 and from 119.1 percent to 18.2 percent for SMA-13, while deviations in the areal roughness parameters Sa and Sq fell by more than 80 percent.

Beyond validation, the framework opened a window onto the particle-scale mechanics of texture formation, and the findings are striking. By classifying particles into size fractions and tracking which ones reach the surface, the researchers found that larger aggregates mainly control surface exposure, while intermediate and small particles dominate the formation of the highest asperities. In AC-13, surface peaks arose from distributed contributions of the 2.36 to 4.75 millimeter and 4.75 to 9.5 millimeter fractions, which together accounted for nearly 98 percent of peak particles, producing a homogeneous texture. SMA-13 showed a decoupled, skeleton-driven pattern in which the 9.5 to 13.2 millimeter fraction was most exposed at the surface, yet the 4.75 to 9.5 millimeter fraction generated 56 percent of the peaks, explaining its broader, more heterogeneous height distribution.

The implications reach well beyond academic curiosity. Because the framework links mixture composition, gradation, and aggregate morphology directly to quantitatively validated surface texture, it transforms pavement design from descriptive measurement into predictive, simulation-driven engineering. Engineers could someday screen candidate mixture designs virtually, optimizing skid resistance and drainage before committing materials to a laboratory or a road. The authors caution that the bridging parameters are numerical rather than intrinsic material properties and may require recalibration for different aggregate lithologies, binders, or interface conditions, and that the computationally demanding polyhedral simulations currently limit the approach to texture reconstruction and microscopic investigation. Even so, the study marks a decisive step toward a future in which the texture of a highway is designed on a computer as deliberately as the shape of an aircraft wing.

Subject of Research: Digital reconstruction of three-dimensional asphalt pavement surface texture using laser-scanned aggregates and the Contact Dynamics method

Article Title: A digital reconstruction framework for three-dimensional asphalt pavement surface texture using the Contact Dynamics method

Article References: Ge, H., Sha, A., Jiang, W., Xing, C., Quezada, J. C., Houerou, V. L., & Chazallon, C. (2026). A digital reconstruction framework for three-dimensional asphalt pavement surface texture using the Contact Dynamics method. Case Studies in Construction Materials, 25, Article e06469. https://doi.org/10.1016/j.cscm.2026.e06469

Image Credits: AI Generated

DOI: 10.1016/j.cscm.2026.e06469

Keywords: asphalt pavement, surface texture, Contact Dynamics, discrete element method, aggregate morphology, 3D laser scanning, skid resistance, macrotexture, virtual compaction, pavement engineering, digital reconstruction, asphalt mastic

The stakes are considerable. Tillage is among the most energy-intensive operations in crop production, and improper machinery use during land preparation can irreversibly damage soil structure. Deep ploughing demands enormous power to fracture compacted horizons, which is why manufacturers have developed machines that pair a passive subsoiler shank with an active, powered rotary tiller. The rotary component pulverizes the soil the subsoiler cracks open, mixes in fertilizer, crop residues and organic amendments, and reduces the number of field passes needed. Fewer passes mean less compaction, less fuel, less labor and less time in the field. But until now, designers have lacked a reliable way to predict exactly what such combined implements do to the soil profile before the steel ever touches the ground.

DEM offers that window. The technique, pioneered in the geotechnical literature since the late 1970s, treats soil not as a continuous medium but as millions of individual particles, solving the equations of motion for each one as they collide, slide and cohere. In this study, the team modeled the soil bed in Altair EDEM 2022 software using the hysteretic spring with linear cohesion contact model, which adds a cohesion resistance to the normal contact forces between particles to mimic the behavior of real clay. The implement itself was designed in PTC Creo Parametric 3D modeling software and imported into the simulation environment, allowing the researchers to replicate the exact geometry of the subsoiler-cum-rotary tiller used in physical tests.

Calibration was the critical step. Because no universally accepted procedure exists for selecting the micromechanical parameters that govern particle interactions, the team anchored their model to laboratory measurements. They worked with clay soil from the experimental soil bin at Nanjing Agricultural University, containing 47.0 percent clay, 36.5 percent silt and 15.5 percent sand, with an average bulk density of 1.5 grams per cubic centimeter. Direct shear tests under unconsolidated undrained conditions yielded cohesion, internal friction angle and shear strength via the Mohr-Coulomb equation, while a digital penetrometer measured penetration resistance at ten locations. Using the angle of repose method and reverse parametrization, the researchers iteratively adjusted restitution and friction coefficients until the simulated soil formed a natural pile at 36.87 degrees, matching the physical material’s behavior.

The validation results are striking. Across eighteen soil bin experiments, the mean horizontal tillage resistance measured by sensors on the three-point linkage of a motorized trolley was 1,950.474 newtons, while vertical resistance averaged 295.92 newtons. The DEM model reproduced these values with a relative error of just 0.4 percent for horizontal resistance and 4.9 percent for vertical resistance. Horizontal resistance predictions achieved a coefficient of determination of 0.9997 with a normalized root mean square error of 0.04, while vertical resistance reached an R-squared of 0.9. An unpaired t-test found no statistically significant difference between simulated and measured values for either component, meaning the virtual experiment was statistically indistinguishable from the real one.

The model also predicted the shape of the furrow left behind. Soil profilometer measurements of the tilled profile matched the simulation with a relative error of 4.3 percent, an R-squared of 0.9936 and a normalized RMSE of 0.23. Both experiment and simulation produced U-shaped furrows with loose soil at the bottom, a profile considered favorable for seed coverage. The average top width of the soil profile was 0.405 meters in the soil bin versus 0.3875 meters in the simulation, and bottom widths were 0.26 and 0.25 meters respectively, a close correspondence that underscores the model’s fidelity.

Perhaps the most consequential finding concerns soil mixing. By coloring the simulated topsoil and subsoil particles differently, the researchers could watch, in effect, inside the soil as the machine passed through. The L-shaped rotary blades promoted predominantly horizontal and downward soil movement with limited upward throw, allowing topsoil particles to migrate toward deeper layers without wholesale inversion of the profile. Even at a tillage depth of 30 centimeters, the natural layering remained discernible, a critical advantage over conventional rotary systems that churn the profile indiscriminately. The mixing of the two layers increased with cutting depth, as expected given the larger soil volume involved, but the vertical-axis rotation design avoided dragging subsoil to the surface, preserving stratification that underpins fertility and moisture retention.

The study also mapped how operating parameters drive energy demand. Both horizontal and vertical resistance rose as tillage depth increased from 0.15 to 0.30 meters, because deeper operation cuts, disperses and moves a greater volume of soil. Resistance likewise grew when forward speed increased from 1.5 to 2.5 kilometers per hour, since faster-moving soil particles gain acceleration, raising normal loads on the tool and thus frictional resistance. The substantial gap between horizontal and vertical forces, roughly 1,800 to 2,060 newtons versus 264 to 323 newtons, shows that far more energy is spent dragging the implement forward than penetrating downward. Notably, the rotary tiller’s rotational motion generated a forward thrust component that offset part of the draft requirement, one reason combined active-passive implements outperform conventional systems in energy efficiency.

The practical implications reach from the design office to the farm field. For manufacturers, the validated model provides a virtual testing platform to optimize blade shape, operating depth and rotational speed across diverse soil conditions without costly physical prototyping. The evidence points toward vertical-axis rotary tillers equipped with L-shaped blades as the configuration of choice for deep tillage that respects soil architecture. For farmers, the combined implement approach promises reduced operational time and fuel consumption while maintaining soil quality, addressing economic and environmental goals simultaneously. As the authors conclude, DEM can serve as an accurate, consistent and fast method for predicting the final soil condition and the resistances required for tillage operations, and with proper blade selection, deep tillage and soil structure conservation need no longer be opposing goals.

Subject of Research: Discrete element simulation of rotary mixing effects on soil structure and tillage resistance for a combined subsoiler and rotary tiller

Article Title: Evaluation of rotary mixing effects on soil structure and tillage resistance using discrete element method

Article References: Makange, N. R., & Ji, C. (2026). Evaluation of rotary mixing effects on soil structure and tillage resistance using discrete element method. Discover Soil, 3(1), Article 148. https://doi.org/10.1007/s44378-026-00308-8

Image Credits: AI Generated

DOI: 10.1007/s44378-026-00308-8

Keywords: discrete element method, tillage resistance, rotary tiller, subsoiler, soil structure, soil bin experiments, EDEM simulation, blade geometry, deep tillage, soil profile, precision agriculture, soil-tool interaction

The case for concern rests on the peculiar character of both the Moon and its soil. The lunar surface combines an almost total vacuum, one-sixth of Earth’s gravity, extreme temperature swings between day and night, intense ionizing radiation, electrostatic charging driven by solar wind and ultraviolet light, and continuous micrometeoroid bombardment. These conditions have shaped regolith into a material unlike anything quarried on Earth: highly angular, poorly sorted, and cohesive in ways that make it reluctant to flow. Prior studies have shown that the sharp particle shapes produce poor flowability that could jam hoppers and silos, while solar-wind charging combined with weak charge dissipation in vacuum can levitate fine particles, creating dust clouds that contaminate optics, abrade seals, and threaten astronaut health. Apollo-era experience with lunar dust, which degraded camera lenses, thermal surfaces, and spacesuits, offers a cautionary preview of what awaits full-scale mining and processing hardware.

Earth-based experiments currently fill the gap as best they can. Researchers use parabolic flights and drop towers to approximate reduced gravity, thermal vacuum chambers to approximate the exosphere, and increasingly sophisticated regolith simulants to stand in for the real thing, while Discrete Element Method, or DEM, computer simulations model particle-by-particle behavior numerically. But the authors point out a fundamental circularity: none of these approaches has ever been validated against actual lunar regolith handled under actual lunar conditions, because no such dataset exists. Existing simulants were largely developed for spectroscopic or chemical-process work, and their material handling characteristics were rarely a design priority. There is, the authors note, no standardized record of basic handling parameters such as flow functions in simulant property databases. The result, documented in international gap assessments, is a substantial risk that equipment designed on Earth will fail or underperform the first time it digs into the real thing.

To address this, the team proposes a pragmatic rather than purely scientific strategy. Instead of measuring fundamental material properties with laboratory precision, the payload would demonstrate and observe eight practical handling processes expected in a future lunar economy: excavation, conveying, sorting, dynamic handling, static handling, electrostatic separation, pile forming, and compaction. The reasoning is that watching real regolith move through machinery that resembles future flight hardware yields both the validation data that Earth-based simulations need and a direct de-risking of the mechanisms themselves. Each subsystem doubles as a technology demonstration, raising the technology readiness level of components that future missions will depend on, while simultaneously generating video observations from which fundamental flow parameters can be derived.

The proposed architecture arranges these eight subsystems in a single cascading sequence, with the output of each process feeding the next, a choice made to minimize mass and volume within the envelope of commercial lunar landers. The sequence begins with a continuous bucket-wheel excavator mounted on a deployable arm, chosen over discrete diggers for its steady material flow and predictable scaling behavior. Excavated regolith is lifted by a two-stage screw conveyor, selected for mechanical simplicity and tolerance of gravity variations, to the top of a gravity-fed stack. There the material first passes through a vibratory sieve carousel, which can either size-separate particles or be bypassed, then into a transparent-walled rotating drum with adjustable speed for studying dynamic flow regimes, next into a wedge hopper with adjustable wall angles and outlet width for static handling tests, past a pair of planar electrodes that generate electric fields across the falling regolith stream, and finally onto a flat plate where pile formation is recorded and a linear actuator applies controlled compression, mimicking brick-making and foundation compaction.

Observation throughout relies on non-contact optical imaging rather than distributed sensor networks. Up to eight cameras, potentially supplemented by a single hyperspectral camera for chemical and mineralogical context, would record every process, an approach the authors argue simplifies development and directly de-risks the video-based process control techniques that full-scale lunar plants will eventually need. Crucially, the payload would also carry roughly five samples of terrestrial regolith simulants, each a few hundred grams, dispensed from a carousel into the processing chain. Watching familiar simulants behave, or misbehave, beside real lunar material under real lunar conditions is what turns the payload from a demonstration into a calibration instrument: any divergence between simulant and native regolith tells modelers exactly how much to trust their Earth-based results. The carousel also provides redundancy, since if excavation fails, the mission can still proceed using delivered samples.

Order-of-magnitude engineering suggests the concept is feasible with current commercial capabilities. The vertically oriented payload is estimated at 79 kilograms with a maximum dimension of 1.6 meters, a peak power draw of 95 watts per experimental cycle, and a data budget dominated by video: roughly 655,000 megabits per full run of eight cameras, compressible below 108,000 megabits and downloadable in about three hours at 10 megabits per second. As a delivery case study, the team examined Firefly Aerospace’s Blue Ghost lander, which offers 150 kilograms of payload capacity at an estimated cost of around one million dollars per kilogram to the lunar surface. Accommodating the payload’s tall gravity-fed stack and its surface-reaching excavator requires combining two of Blue Ghost’s payload bays, but the study’s indicative computer-aided design shows the configuration fits.

Mission planning assumes a static lander at low to mid latitudes, designed to survive the lunar night, operating across six lunar days. A single day of operations proved too little to return meaningful data, so the baseline plan divides the mission into phases: commissioning during the first day, then repeated 96-hour experimental cycles in which each four-hour sample-handling sequence is followed by downlink, ground analysis, and refinement of the next run. Over six lunar days, the payload could complete sixteen experimental cycles, processing sixteen samples in an iterative fail-fast loop that mirrors the agile development philosophy the authors advocate for the payload’s construction. Landing site selection involves a trade-off between the scientifically compelling but poorly characterized lunar south pole, where future ISRU activity will concentrate, and previously visited regions where existing ground-truth regolith data would allow direct comparison, and future variants could add fetch rovers to sample across kilometer scales.

Beyond validation, the study identifies three collateral payoffs: practical knowledge of dust generation, transport, and mitigation, including testing wipers, electrostatic repellers, and optical coatings on the cameras themselves; early measurement of mechanical wear on handling hardware in the abrasive lunar environment, using wear indicators, power monitoring, and vibroacoustic sensing; and the elevation of technology readiness for a suite of components future missions would otherwise fly unproven. The authors present the paper explicitly as a conversation starter, inviting the community to debate the approach, refine the design, and pursue laboratory breadboards of the subsystems for testing in vacuum and parabolic-flight facilities. If the concept survives that scrutiny, it could split into smaller sub-payloads distributed across multiple lander missions. Either way, the argument lands squarely: before humanity mines the Moon, it should first learn how the Moon’s dust actually moves, and the cheapest way to find out is to ship a small factory’s worth of handling hardware to the surface and watch.

Subject of Research: In situ validation and calibration of lunar regolith granular materials handling for future lunar in-situ resource utilization.

Article Title: Outline case for a payload and mission scenario to perform in situ de-risking, validation and calibration of lunar granular materials handling

Article References: Pratnekar, M., Cilliers, J. J., Crawford, I. A., Hadler, K., Hartlieb, P., Joy, K. H., Patel, M. R., Saydam, S., Sureda, M., Bardaux, M., Castagnaro, D. B., Diakonikolis, M., Fossey, J., Gee, M. J., Jhamb, E., Kanda, B., Marechal, Q., Monteiro, N., Ray, T., … Cullen, D. C. (2026). Outline case for a payload and mission scenario to perform in situ de-risking, validation and calibration of lunar granular materials handling. Space and Planetary Resources, 2(1), Article 1. https://doi.org/10.1007/s44461-026-00006-x

Image Credits: AI Generated

DOI: 10.1007/s44461-026-00006-x

Keywords: lunar regolith, in-situ resource utilization, granular materials handling, reduced gravity, lunar lander, payload design, discrete element method, regolith simulants, lunar dust, CLPS, Blue Ghost, lunar ISRU