The researchers used weathered coal gangue that had been stored in the open air for two years at the Jilin Coal Energy Reserve Base in China. The particles were light gray, rough, loosely structured and unevenly graded, with most concentrated in the 9.5–19.0 mm range. Untreated material had a crushing index of 23.96%, above the 20% limit for Class II crushed stone specified in GB/T 14685-2022, and was therefore considered unsuitable for direct use as a pavement-concrete aggregate. Its other measured properties were a 28.61% Los Angeles wear rate, 24.86% firmness index, 84.32 kgf/mm² Vickers hardness and 7.32% aggregate impact value.

The treatment combined a physical cement coating with chemical hardening. Cement paste was prepared at a water-to-cement ratio of 0.75, and 1.5% magnesium aluminum silicate was added to stabilize the paste and suppress bleeding. Weathered gangue was mixed into the paste for two minutes, soaked for 30 minutes, removed and air-dried for two days. A water-based lithium-based curing agent, diluted at a curing-agent-to-water ratio of 1:2, was then sprayed evenly over the particle surfaces. After another 30 minutes of room-temperature drying, the material was cured for 28 days. The agent’s lithium silicate can react with calcium hydroxide in the cement coating to form calcium silicate hydrate, while cement reactions can produce calcium silicate hydrate and calcium aluminate hydrate on and beneath the aggregate surfaces.

After consolidation and 28 days of curing, the gangue’s crushing index declined from 23.96% to 16.05%, placing it below the cited Class II threshold. Vickers hardness rose from 84.32 to 141.43 kgf/mm², Los Angeles wear loss fell from 28.61% to 19.88%, and the aggregate impact value declined from 7.32% to 5.66%. These laboratory measurements showed greater hardness and resistance to wear and impact-induced fragmentation. Density increased from 2.4751 to 2.619 g/cm³, while water absorption rose slightly from 4.32% to 4.82%, meaning that the treatment strengthened the particles without removing their porous character.

To examine how particle size affected concrete behavior, the team prepared one natural-stone control and four mixtures in which consolidated weathered gangue progressively replaced conventional aggregate over wider size intervals. The Fuller target gradation contained 0–2.36, 2.36–4.75, 4.75–9.50, 9.50–19.0 and 19.0–31.5 mm fractions. The gangue mixtures cumulatively replaced conventional aggregate through 2.36–4.75, 2.36–9.50, 2.36–19.0 and 2.36–31.5 mm, representing 11.46%, 27.55%, 50.29% and 72.63% of the total aggregate. The 0–2.36 mm fraction was not replaced, and the study did not separately test different replacement ratios within an individual size fraction.

Each mixture and specimen type included four replicates, with reported values and force–displacement data calculated as arithmetic means. According to GB/T 50081-2019, 100 mm cubes were used for compressive and shear testing, 150 mm cubes for splitting tensile testing and 100 by 100 by 400 mm prisms for flexural testing. The pavement mix contained 315 kg/m³ of cement, 120 kg/m³ of water, 1% water-reducing admixture, 632 kg/m³ of fine aggregate and 1,283 kg/m³ of coarse aggregate. Specimens were tested after 28 days, using a loading rate of 0.5 MPa/s for compression and shear and 0.05 MPa/s for splitting tension and flexure.

Under the study’s secondary-road criteria, the maximum admissible replacement extended from 2.36 to 19.0 mm. At that setting, the concrete achieved 27.81 MPa compressive strength, 2.13 MPa splitting tensile strength, 4.67 MPa flexural strength, 2.93 MPa cohesion and an internal friction angle of 66.20°. Replacing only the 2.36–4.75 mm fraction yielded flexural and splitting tensile strengths of 5.24 and 3.13 MPa, compared with 5.22 and 3.08 MPa in the control. Because the splitting-strength difference was only about 1.6% and the error bars overlapped, the researchers interpreted it as essentially unchanged. The 2.36–9.50 mm mixture also retained 5.18 MPa flexural strength, above the study’s heavy- and higher-traffic criterion of more than 5.0 MPa.

Mechanical performance generally weakened as larger gangue fractions entered the concrete. Splitting tensile strength was 2.78 MPa for the 2.36–9.50 mm replacement, 2.13 MPa for 2.36–19.0 mm and 1.52 MPa for replacement through 31.5 mm. The latter two values represented reductions of 30.8% and 50.6% relative to the unreplaced control. Flexural strength fell to 3.44 MPa in the fully replaced mixture, 34.1% below the control. The researchers associated the reductions with the lower strength and higher porosity of coarse gangue, which promoted aggregate fracture and stress concentration. They also observed that gangue absorbed water during curing, potentially lowering the local water-to-cement ratio and strengthening the aggregate–mortar bond.

Failure morphology changed alongside the strength measurements. Natural-aggregate concrete developed an X-shaped fracture that began near the corners and propagated through interfacial transition zones, or ITZs, toward the center. In gangue mixtures, cracks increasingly traveled through the gangue as well as the ITZ and mortar, and the overall response became more plastic. Under full replacement, the outermost large gangue particles failed first, after which damage progressed inward. Splitting-tensile failure displacement decreased from 10.43 mm in the control to 4.19 mm in the fully replaced group, indicating reduced deformation capacity. Cohesion declined from 4.23 MPa in the control to 1.75 MPa at full replacement, while the internal friction angle increased to 71.43°; the authors noted that this occurred alongside deterioration in other mechanical properties.

A two-dimensional finite-element model represented the 100 mm compression specimens with 40,000 CPS4R elements and four material phases: gravel, mortar, consolidated gangue and a weakened ITZ. Simulated compressive strengths exceeded the corresponding laboratory means by 5.61–10.68%. For example, the model estimated 44.62 MPa for the control, compared with a measured mean of 41.32 MPa, and 23.68 MPa at full replacement, compared with 21.41 MPa. The study attributes the difference partly to the idealized model, which does not reproduce specimen unevenness and machine error and cannot fully capture thickness-direction behavior, lateral diffusion or all cracking effects. Despite these limitations, the simulations reproduced the observed shift in failure location and crack path.

At the maximum replacement range of 2.36–19.0 mm, the model indicated that natural aggregate in the 19.0–31.5 mm fraction carried 58.31% of the stress contribution. The corresponding shares were 22.88% for 9.50–19.0 mm, 13.43% for 4.75–9.50 mm and 5.38% for 2.36–4.75 mm. Mean stress was 22.54 MPa in the largest natural-stone fraction, compared with 9.30, 9.77 and 9.68 MPa in the progressively smaller fractions. The analysis supports a gradual replacement strategy under the tested conditions: consolidated weathered gangue can occupy smaller aggregate fractions while the largest conventional stones provide much of the modeled load-bearing capacity. Durability under traffic, repeated loading, environmental exposure and field-scale construction was not evaluated, leaving those questions beyond the evidence presented by this laboratory study.

Subject of Research: Mechanical performance of mining-area pavement concrete containing consolidated weathered coal gangue

Article Title: Mechanical behaviors of weathered coal gangue filled concrete pavement based on surface solidification

Article References: Li, Y., Guo, W., Miao, X., Tao, X., Wang, Q., Zhang, J., Chen, W., & Wei, Z. (2026). Mechanical behaviors of weathered coal gangue filled concrete pavement based on surface solidification. Frontiers in Materials, 13, Article 1954367. https://doi.org/10.3389/fmats.2026.1954367

Image Credits: AI Generated

DOI: 10.3389/fmats.2026.1954367

Keywords: weathered coal gangue, concrete pavement, surface consolidation, lithium silicate, recycled aggregate, compressive strength, flexural strength, failure modes, finite-element simulation, mining infrastructure, Mechanical, behaviors

The research team, led by Zhanpeng Liu and Wensheng Xiao, combined failure mode and effects analysis, Bayesian networks, risk matrices, and fuzzy comprehensive evaluation into one integrated method. Each technique has well-known strengths and blind spots. FMEA can systematically identify potential failure modes but is highly subjective. Bayesian networks excel at quantitative forward and backward inference yet cannot judge how severe a failure’s consequences would be. Risk matrices offer a visual representation of occurrence and severity but cannot assess the risk level of the system as a whole. Fuzzy comprehensive evaluation handles vague, uncertain information but again depends on subjective judgment. By weaving these approaches together, the researchers aimed to let each method compensate for the weaknesses of the others, producing a single assessment that yields both the system failure probability and the risk level of every individual failure mode.

The workflow begins with a fault tree analysis of the motor, in which twenty bottom-event failure modes across six components, the rotor, bearings, windings, permanent magnets, controller, and mechanical seal, are mapped into a Bayesian network. Prior probabilities are derived from historical failure rates in the OREDA offshore reliability database, adjusted upward by expert judgment to reflect the brutal conditions of underground drilling. Bayesian forward inference then computes the overall system failure probability, while backward diagnostic inference produces posterior probabilities revealing which failure modes are most likely responsible when the system fails. In the baseline assessment, the system failure probability came out at 0.428, a strikingly high figure that underscores how punishing the drilling environment is for these machines.

Next comes importance analysis, a mathematical step that quantifies how much each failure mode contributes to system failure. The team calculated three indices: probabilistic importance, which measures the impact of a component’s failure probability on system failure; structural importance, which reflects a component’s position in the system architecture; and critical importance, which combines both failure probability and sensitivity. Because all twenty failure modes feed the system through identical OR-gate logic, their structural importance is equal, but their probabilistic and critical importance diverge dramatically. Rotor breakage, for instance, posted a probabilistic importance of 1.3782, far above its peers, while bearing wear registered a posterior probability of 0.612, the highest of any mode, simply because bearings grind against each other every moment the motor spins.

These quantitative outputs then feed the qualitative side of the framework. The posterior probability of each failure mode determines its occurrence rating on a five-level scale, from improbable to frequent, while the critical importance determines its severity rating, from negligible to catastrophic. The two ratings fuse into a composite risk index called ROS, weighted 0.4 toward occurrence and 0.6 toward severity, a deliberate choice reflecting the safety-critical nature of drilling, where even rare events with devastating consequences demand priority attention. The resulting ROS values map onto a fuzzy evaluation matrix through a parabolic membership function, and the weights of that matrix come directly from the importance analysis rather than from pure expert opinion, reducing the subjectivity that plagues conventional assessments.

The baseline results identified winding phase-to-phase short circuit as the single highest-risk failure mode, with a ROS value of 4.2 placing it firmly in the high-risk category. Rotor dynamic eccentricity and permanent magnet partial demagnetization followed at 3.6 each, classed as medium-high risk. Perhaps more revealing were the failure modes with severe consequences but vanishingly low occurrence, including rotor breakage, bearing breakage, and permanent magnet breakage, all rated medium risk despite being improbable. The framework’s dual weighting captured exactly the kind of nuance that pure probability-based methods miss: a rare failure that destroys the motor deserves as much managerial attention as a common one that merely degrades performance. The fuzzy evaluation concluded that the system sat at medium risk with a membership probability of 0.498, but with a worrying 0.351 probability of medium-high risk.

Armed with this diagnosis, the researchers proposed targeted countermeasures for every failure mode, ranging from corona-resistant enameled wire and vacuum pressure impregnation for the windings to hastelloy springs and fluorocarbon or EPDM elastomers for the seals, matched to oil-based and water-based mud chemistries respectively. For winding faults, they applied weak magnetic current injection, multi-phase redundancy, and winding redundancy, allowing the motor to shed faulty phases gracefully while maintaining partial power. After implementing the improvements, the system failure probability dropped from 0.428 to 0.394, the mean time between failures rose from 1,847 to 2,032 hours, and the medium-high risk membership probability fell from 0.351 to 0.245. The highest-risk failure mode, the winding short circuit, saw its ROS value plunge from 4.2 to 3.2, a 23.8 percent risk reduction.

What distinguishes this study from earlier hybrid approaches is its unusually rigorous treatment of uncertainty and validation. The team ran a Monte Carlo simulation with 10,000 samples, assuming failure rates follow log-normal distributions with a 20 percent coefficient of variation, and found that the 95 percent confidence intervals of key outputs remained narrow, less than 15 percent of their mean values. A perturbation analysis of the weighting coefficients showed that risk rankings stay stable, with Spearman rank correlations above 0.88, as long as severity retains reasonable dominance, confirming the wisdom of the 0.4-0.6 split. The importance-based weights were cross-checked against the entropy weight method and the CRITIC method, with Kendall’s coefficient of concordance reaching 0.82, indicating strong agreement between expert judgment and data-driven weighting. A paired bootstrap test and a Wilcoxon signed-rank test both confirmed that the post-improvement risk reduction was statistically significant, with p below 0.001.

The authors also introduced three methodological innovations that broaden the framework’s reach. A risk coupling coefficient quantifies how strongly each failure mode couples with environmental loads such as vibration, high temperature, current fluctuations, and high-pressure drilling fluid, distilled from a multi-factor coupling matrix that identified twenty high-probability coupled failure modes. An adaptive risk transfer function models nonlinear propagation from component-level risk to system-level risk, capturing synergistic effects that simple OR-gate logic overlooks. And a risk entropy metric measures how dispersed or concentrated the system’s risk profile is; it fell from 1.42 before improvement to 1.24 after, signaling reduced uncertainty in the risk landscape.

The framework is not without limitations, which the authors acknowledge candidly. Integrating four methodologies creates a complex model with substantial computational overhead, and exact Bayesian inference scales exponentially with the number of failure modes, though hierarchical decomposition into sub-networks reduces the complexity from an intractable O(2^100) to a manageable O(10 times 2^10) for a hundred-mode system split across ten components. Prior probabilities still lean on the OREDA database and expert experience, which update infrequently, and the static Bayesian network does not yet ingest real-time sensor data. Future work, the team suggests, will move toward dynamic Bayesian networks fed by live monitoring evidence, adaptive neuro-fuzzy membership functions optimized by backpropagation, and hierarchical architectures spanning failure mode, component, subsystem, and system layers. For now, the study offers drilling engineers something genuinely valuable: a single, statistically validated assessment that tells them not just whether their motor is likely to fail, but exactly which of twenty failure modes deserves their attention first, and how much safer each repair will make it.

Subject of Research: Hybrid fuzzy risk assessment of drilling permanent magnet synchronous motors using FMEA and Bayesian networks

Article Title: An improved fuzzy risk assessment method fusing FMEA and BN for drilling permanent magnet synchronous motors

Article References: Liu, Z., Xiao, W., Cui, J., Wang, H., & Mei, L. (2026). An improved fuzzy risk assessment method fusing FMEA and BN for drilling permanent magnet synchronous motors. Results in Engineering, 32, Article 113047. https://doi.org/10.1016/j.rineng.2026.113047

Image Credits: AI Generated

DOI: 10.1016/j.rineng.2026.113047

Keywords: risk assessment, FMEA, Bayesian network, fuzzy comprehensive evaluation, permanent magnet synchronous motor, drilling motor, reliability engineering, risk matrix, Monte Carlo simulation, importance analysis, oil and gas drilling, failure modes