The research team, led by Worathep Sae-Long and Thanet Thongdetsri of the University of Phayao in Thailand, together with collaborators at Prince of Songkla University, King Mongkut’s University of Technology North Bangkok, Khon Kaen University, Rajamangala University of Technology Srivijaya, Burapha University and Kasetsart University, set out to test whether a waste-derived bio-fibre could compensate for the well-known mechanical weaknesses of recycled aggregate concrete. Their findings suggest that, with the right fibre treatment and dosage, it can.

The experimental programme was deliberately ambitious in its scope. Natural coarse aggregate was replaced by recycled concrete aggregate at 0% and 100% by volume, pushing the concept to its limit rather than settling for the partial substitution rates common in practice. Water hyacinth fibres were then added at 0.5% and 1.0% by weight of cement, and four distinct fibre categories were compared: treated bark, untreated bark, treated core and untreated core fibres. This matrix of variables allowed the researchers to isolate the effects of aggregate source, fibre dosage, fibre origin within the plant, and surface treatment on the resulting concrete performance.

The environmental arithmetic of the mix design is striking on its own. By adopting 100% recycled coarse aggregate, each cubic metre of concrete avoided the extraction of approximately 906.91 kilograms of natural stone, while the fibre additions converted between 1.67 and 3.33 kilograms of invasive aquatic biomass per cubic metre into structural reinforcement. Water hyacinth, which reproduces so aggressively that it can double its coverage in weeks and block navigation, hydropower intakes and irrigation channels, is usually a disposal burden. Here it becomes a raw material with a genuine engineering function.

The mechanical results tell a nuanced story of trade-offs. Replacing all natural coarse aggregate with recycled material reduced the 28-day compressive strength by between 12.81% and 25.56%. The culprit, confirmed by microstructural observations, lies in the interfacial transition zones: the thin layers of paste that bond aggregate to matrix. Recycled aggregates carry remnants of old mortar on their surfaces, creating weaker and more heterogeneous transition zones that fail earlier under load than the dense interfaces formed around natural stone.

Adding water hyacinth fibres deepened that compressive penalty. Depending on the fibre type and dosage, compressive strength dropped by a further 20.94% to 48.58%. The authors attribute this mainly to fibre-induced voids, reduced compaction of the fresh mix, and local discontinuities in the cement matrix where fibre clusters disrupted the paste structure. Organic fibres are also hydrophilic, and their tendency to absorb water and clump during mixing makes workability a persistent challenge at higher dosages. In compression, where failure is governed by matrix density and continuity, the fibres simply get in the way.

Where the fibres shine is in tension and flexure, the properties that matter most for cracking resistance and structural resilience. Splitting tensile strength increased by 35.76% to 64.24% compared with unreinforced recycled aggregate concrete, a gain the researchers link to crack bridging and stress transfer across developing cracks. When a crack opens in fibre-reinforced concrete, fibres spanning the crack carry load that would otherwise tear the matrix apart, delaying failure and softening the material’s response.

The standout performer was treated bark fibre at the 0.5% dosage. This combination delivered the best flexural response of the entire study, achieving a flexural strength of 5.39 megapascals and increasing flexural toughness by 56.97%. Toughness, the total energy a beam absorbs before failure, is arguably the more meaningful figure: it measures not just how much load the concrete resists but how gracefully it deforms. The researchers attribute the superior performance of treated bark fibres to their higher intrinsic tensile strength, fibrillated surface texture, and improved bonding with the surrounding cement matrix, all of which help fibres anchor themselves and pull out gradually rather than slipping or snapping prematurely.

Beyond the laboratory tests, the team developed empirical equations to predict strength within the investigated range and built a nonlinear finite element model to simulate the structural behaviour of the fibre-reinforced recycled concrete. The model reproduced measured peak loads with errors of just 2.90% to 5.74%, an encouraging level of accuracy that suggests existing computational tools can be adapted to design with these unconventional materials. The authors caution, however, that their predictive equations are valid only within the range of variables they tested, so engineers should not extrapolate them to other mix proportions or fibre contents without further validation.

The broader significance of the study lies in its demonstration that two waste streams, one mineral and one biological, can be combined into a construction material with genuinely useful structural properties. Fully recycled aggregate concrete has long been viewed with scepticism by structural engineers because of its strength and durability penalties, and natural fibre reinforcement has often been dismissed as a laboratory curiosity. By showing that treated water hyacinth bark fibres can boost tensile resistance and toughness in a mix containing no natural stone at all, the Thai team offers a template for circular-economy construction in regions where both demolition waste and invasive biomass are abundant. The compressive strength reductions remain a real limitation that will confine early applications to non-structural or lightly loaded elements, pavements and repair mortars rather than high-rise columns. But as a proof of concept, the work makes a compelling case that the weeds clogging the world’s lakes may hold part of the answer to making concrete more sustainable, one cubic metre at a time.

Subject of Research: Mechanical properties of recycled aggregate concrete reinforced with water hyacinth fibres

Article Title: Experimental investigation of the mechanical properties of recycled aggregate concrete reinforced with water hyacinth fibres for sustainable construction

Article References: Sae-Long, W., Thongdetsri, T., Chompoorat, T., Limkatanyu, S., Sukontasukkul, P., Pannachet, T., Boonpichetvong, M., Yaibok, C., Imjai, T., & Pimanmas, A. (2026). Experimental investigation of the mechanical properties of recycled aggregate concrete reinforced with water hyacinth fibres for sustainable construction. Environmental Science and Pollution Research, 33(28), 14470-14504. https://doi.org/10.1007/s11356-026-38179-9

Image Credits: AI Generated

DOI: 10.1007/s11356-026-38179-9

Keywords: recycled aggregate concrete, water hyacinth fibres, natural fibre reinforcement, sustainable construction, compressive strength, splitting tensile strength, flexural toughness, interfacial transition zone, finite element modelling, microstructure, invasive biomass, circular economy

The research, conducted by Ghazi Bahroz Jumaa of the Civil Engineering Department at the University of Garmian in the Kurdistan Region of Iraq, tackles one of the most safety-critical questions in reinforced concrete design: how much shear force can a beam resist before it fails catastrophically? Shear failure is notoriously brittle and sudden, unlike the gradual, warning-giving flexural failure that designers prefer to govern. For beams without stirrups, the closed steel hoops that normally provide shear reinforcement, the entire resistance must come from the concrete itself, making accurate prediction of that resistance a matter of structural life and death.

The heart of the problem lies in the messy, heterogeneous nature of RCA. When old concrete is crushed, each recycled particle is a composite: natural stone wrapped in a layer of adhered mortar that is often weaker, more porous, and riddled with micro-cracks from the crushing process. In a loaded beam, diagonal cracks propagate through the concrete web, and the rough crack faces resist sliding through a mechanism engineers call aggregate interlock. Weaker, more irregular recycled particles interlock less effectively, and the old mortar attached to them introduces additional weak zones. The result is a material whose shear behavior is inherently more variable than that of natural-aggregate concrete, and whose performance depends on the percentage of recycled content, a variable that most established design equations were never formulated to include.

Existing design provisions, such as those in the American Concrete Institute’s ACI 318 code and Europe’s Eurocode 2, were calibrated decades ago on natural-aggregate concrete. When applied to RCA beams, they can be either unconservative or wastefully conservative, and the experimental literature over the past two decades, from studies by Han and colleagues in 2001 through work by Rahal and Alrefaei, Ignjatović and co-workers, and many others, has documented a wide scatter in measured shear strengths. Empirical equations such as the classic Zsutty formulation, dating back to 1971, capture broad trends but struggle with the added complexity of recycled content. This is precisely the kind of nonlinear, multi-variable prediction problem where machine learning has begun to shine.

Jumaa assembled a database of 179 experimental RCA beam specimens drawn from published laboratory studies spanning more than two decades. Each specimen was characterized by the key parameters that govern shear capacity: beam width, effective depth, the shear span-to-depth ratio, the longitudinal reinforcement ratio, the concrete compressive strength, and, crucially, the percentage of recycled concrete aggregate replacing natural stone. The measured shear capacities ranged from small laboratory beams failing at around 12 kilonewtons to massive 400-millimetre-wide specimens carrying more than 860 kilonewtons, giving the models a rich and demanding training landscape.

Five machine learning algorithms were trained and compared under a rigorous validation protocol: k-Nearest Neighbors, Random Forest, Support Vector Machines, Gaussian Process Regression, and Artificial Neural Networks. Rather than relying on a single train-test split, which can flatter a model by luck of the draw, the study employed a 10-fold cross-validation scheme repeated 20 times. In this procedure the data are repeatedly shuffled and divided into ten subsets; the model trains on nine and is tested on the held-out tenth, with the process cycled through all folds and then repeated from scratch twenty times to average out randomness. This is the gold standard for demonstrating that a model’s accuracy reflects genuine learning of underlying physics rather than memorization of individual specimens.

The results were striking. Support Vector Machines emerged as the top performer, achieving a coefficient of determination of 0.933, meaning the model explained more than 93 percent of the variance in measured shear strength, with a root mean square error of 29.98 kilonewtons and a mean absolute error of just 13.08 kilonewtons. Gaussian Process Regression followed closely with an R-squared of 0.912 and the lowest mean absolute error of the trio at 11.81 kilonewtons, while Artificial Neural Networks achieved an R-squared of 0.892. All three outperformed both the newly proposed empirical equations, including a modified Zsutty equation and an ACI 318-style equation incorporating RCA content, and the traditional design codes themselves. Support Vector Machines, which work by mapping inputs into a high-dimensional feature space where a maximum-margin boundary separates the data, proved especially adept at handling the nonlinear interactions between beam geometry, material strength, and recycled content.

Beyond raw accuracy, the study included a parametric analysis that interrogated how the trained models respond when individual variables change, a crucial sanity check for any data-driven engineering tool. The machine learning predictions reproduced physically sensible trends: shear capacity increased with beam depth and width, and decreased as the recycled aggregate content rose, consistent with the known degradation of aggregate interlock in RCA concrete. This alignment between statistical learning and structural mechanics matters enormously, because a black-box model that contradicts physical intuition would be dangerous to trust, no matter how impressive its error metrics appear on paper.

The practical implications reach well beyond the laboratory. As governments worldwide push circular-economy mandates and green building standards, the volume of RCA entering structural applications is set to grow sharply, and the fib Model Code 2020 and the next generation of Eurocode 2 are actively working toward codified design rules for recycled aggregate concrete structures. Reliable predictive tools are the prerequisite for such codification: engineers need confidence that a beam designed with, say, 50 or 100 percent recycled coarse aggregate will carry its intended shear load with the same margin of safety as a conventional beam. A validated machine learning model, trained on nearly two hundred real experiments, offers exactly that confidence, and could inform both code committees drafting new provisions and practitioners evaluating existing structures built with sustainable materials.

The study also fits into a broader movement sweeping through structural engineering, in which data-driven methods complement, and sometimes surpass, hand-derived empirical formulas. Researchers have already applied neural networks and related techniques to predict the shear capacity of fiber-reinforced polymer concrete beams, the compressive strength of lightweight and recycled concretes, and bond strength in reinforced concrete members. What distinguishes the present work is its focus on the specific, under-addressed case of stirrup-free RCA beams and its head-to-head comparison of five algorithms under a demanding cross-validation protocol. As Jumaa notes in the paper, the work represents a step forward in using machine learning to enhance structural engineering, with the potential to make a significant impact in the field of sustainable materials. If the construction industry is to recycle its own waste at scale, it may well need algorithms, as much as new mix designs, to make the resulting structures safe.

Subject of Research: Machine learning prediction of shear strength in reinforced recycled aggregate concrete beams without stirrups

Article Title: Shear strength prediction of reinforced recycled aggregate concrete beams without stirrups using soft computing and empirical methods

Article References: Jumaa, G. B. (2026). Shear strength prediction of reinforced recycled aggregate concrete beams without stirrups using soft computing and empirical methods. Neural Computing and Applications, 38(19), Article 771. https://doi.org/10.1007/s00521-026-12488-z

Image Credits: AI Generated

DOI: 10.1007/s00521-026-12488-z

Keywords: machine learning, recycled concrete aggregate, shear strength, reinforced concrete beams, stirrups, Support Vector Machines, artificial neural networks, Gaussian Process Regression, ACI 318, Eurocode 2, sustainable construction, structural engineering

The research, published in the journal Discover Geoscience, is notable for connecting what happens at the scale of individual mineral grains to the performance of a roadbed a driver can actually feel. Led by Mohammed Abdulkareem Adisa of Kwara State University, the team combined laboratory geotechnical testing with X-ray diffraction and scanning electron microscopy to trace exactly how recycled concrete changes the internal architecture of tropical laterite. The work addresses a gap in the existing literature: most previous RCA-soil studies have examined temperate-region soils or engineered fills, while lateritic soils, which are quartz-rich, iron-oxide-bearing and relatively low in clay minerals, have received far less attention despite being the dominant subgrade material across Nigeria and neighboring countries.

The lateritic soil for the experiments was excavated from a pit along the access route to the University of Ilorin’s permanent campus in Kwara State, an area of humid tropical climate receiving roughly 1,200 millimeters of rain annually. The soil proved to be a medium-plasticity, fines-dominated material, classified as AASHTO A-7-6 and USCS CL, essentially a brown lateritic lean clay with sand. Its specific gravity was 2.72, its liquid limit 42.5 percent, and its plasticity index 18.3. The recycled concrete aggregate, by contrast, was recovered from demolished reinforced concrete slabs and beams at a construction site in the same state, crushed in a laboratory jaw crusher and sieved to particles between 4.75 and 20 millimeters. Gradation testing showed the RCA to be a well-graded gravel, a sharp complement to the poorly graded, fines-rich laterite.

X-ray diffraction revealed the mineralogical story behind the pairing. The laterite produced a sharp, high-intensity quartz peak near 26.6 degrees two-theta, together with minor feldspar peaks and clear signals from hematite, the iron oxide responsible for the soil’s reddish-brown color. Weak kaolinite reflections indicated that clay mineral content was low. The RCA diffractogram was distinctly different, dominated by quartz but also containing anorthite, a calcium-rich feldspar, and albite, phases consistent with the cementitious chemistry of demolished concrete. These calcium-bearing minerals hint at the possibility of secondary cementitious or pozzolanic reactions when the aggregate meets clay-bearing soil, although the authors are careful to note that any such chemical contribution was not independently verified in this study.

The mechanical results were unambiguous. In Standard Proctor compaction tests, the untreated control soil achieved a maximum dry density of 1.98 grams per cubic centimeter at an optimum moisture content of 9.5 percent. As RCA was added in five-percent increments, density climbed steadily: 2.01 at five percent, 2.04 at ten percent, 2.09 at fifteen percent, and a peak of 2.11 grams per cubic centimeter at twenty percent RCA, while the optimum moisture content fell to just 8.0 percent. The improvement arises because coarse, high-specific-gravity RCA particles occupy voids within the clay structure, forming a stiffer, better-interlocked skeleton that requires less water for optimal packing, since the crystalline, silica-rich recycled material absorbs less moisture than plastic clay particles.

Bearing capacity followed the same trajectory. In California Bearing Ratio tests, the untreated soil recorded 58 percent unsoaked and 30 percent soaked. At twenty percent RCA, those figures rose to 74 percent unsoaked and 41 percent soaked, a substantial gain in the load-bearing performance that governs pavement design. Scanning electron microscopy provided the visual explanation: micrographs at 500-times magnification showed large, angular RCA fragments with rough, porous surfaces bearing adhered lateritic fines, while images at 1,000 times revealed fine particles from the hematite-rich soil and RCA dust partially filling the intergranular voids between rigid quartz and feldspar grains. The result is a dense, mechanically interlocked fabric with reduced pore volume and enhanced particle-to-particle friction, which translates directly into the improved density and strength measured in the laboratory.

Intriguingly, the benefits did not continue rising indefinitely. At twenty-five percent RCA, performance reversed: maximum dry density slipped to 2.08 grams per cubic centimeter, optimum moisture content jumped to 11.0 percent, and both soaked and unsoaked CBR values declined. The authors attribute this reversal to several compounding mechanisms. Beyond twenty percent, the proportion of coarse and mortar-adhered RCA particles exceeds what the remaining clay-silt fraction can effectively bind, disrupting the continuity of the fine matrix. Excess RCA particles begin contacting one another directly, producing a coarser, more open skeleton that traps voids the fines can no longer fill. Meanwhile, the old adhered cement mortar on recycled particles is inherently porous, so higher dosages raise the water demand sharply, promoting segregation during compaction. The lesson is that more recycled material is not always better; dosage matters, and there is a genuine optimum.

The environmental and economic case may prove the study’s most consequential contribution. In a screening-level assessment normalized to one tonne of treated material, a conventional five-percent cement stabilization scheme was estimated to emit 52.7 kilograms of CO2, whereas the optimized twenty-percent RCA mixture emitted only 3.6 kilograms, an indicative reduction of roughly ninety-three percent within the system boundary considered. Material costs also favored RCA, with improvements running nine to forty-six percent cheaper per tonne than cement-based methods across the tested dosages, and the twenty-percent blend offering the best balance of performance gain against cost. The authors stress these figures are preliminary, based on industry-average emission factors and assumed transport distances, and recommend a full life-cycle assessment with site-measured data before the results feed into specifications or certification decisions.

The researchers are equally candid about the study’s limits. Testing covered immediate strength only; long-term durability under repeated wetting-drying cycles typical of tropical climates was not assessed, nor was the potential for slow strength gain from residual cementitious phases in the RCA through extended curing. No leachate analysis was performed, leaving open the question of whether recycled concrete might introduce sulfates, chlorides or other contaminants to groundwater. And because the findings apply to one specific quartz-rich, low-kaolinite laterite, they may not transfer directly to more plastic or clay-rich tropical soils. Future work, the team suggests, should include unconfined compressive strength, triaxial shear, resilient modulus, permeability and durability testing.

Even with those caveats, the study lands at a moment of urgent need. Construction and demolition waste constitutes an estimated twenty to fifty percent of municipal solid waste in industrialized nations, with China alone generating nearly 2.36 billion tonnes in 2020, and conflict zones such as Ukraine adding more than 100 million tonnes of rubble to the global burden. Turning even a fraction of that stream into road subgrade material, at a dosage that measurably improves strength while slashing emissions and cost, offers a concrete demonstration of circular-economy principles applied beneath our wheels. For Nigeria and other rapidly urbanizing tropical nations, where infrastructure demand and demolition waste are both rising sharply, the message is that the strongest foundation for new roads may already be lying in the rubble of the old ones.

Subject of Research: Stabilization of tropical lateritic subgrade soil using recycled concrete aggregate for sustainable road construction

Article Title: Compaction, CBR, and microstructural performance of lateritic soil stabilized with recycled concrete aggregate

Article References: Adisa, M. A., Omolara, K. E., Maurel, H. T., Tomiwa, A. H., Ndane, S. P., Amao, A. O., & Kayode, Y. A. (2026). Compaction, CBR, and microstructural performance of lateritic soil stabilized with recycled concrete aggregate. Discover Geoscience, 4(1), Article 347. https://doi.org/10.1007/s44288-026-00718-9

Image Credits: AI Generated

DOI: 10.1007/s44288-026-00718-9

Keywords: recycled concrete aggregate, soil stabilization, lateritic soil, CBR, compaction, XRD, SEM, circular economy, sustainable roads, CO2 emissions, geotechnical engineering, construction and demolition waste