Search Results (124)

The safe disposal of high-level radioactive waste (HLW) is a significant challenge in the nuclear industry. As the buffer backfill material for deep geological disposal engineering barriers, the shrinkage characteristics of bentonite–sand mixtures are critical to the long-term stability of repositories. This study systematically conducted drying shrinkage tests using an improved thin-film technique under varying sand contents R_s (0–50%), salt solution concentrations (0–1.5 mol/L), and ion types (Na⁺, Mg²⁺, Ca²⁺, Cl⁻, SO₄²⁻). The mechanisms of the effects of sand content and salt solutions on the shrinkage behavior of bentonite were revealed based on the results. In addition, the rationality of the MCG-B model in simulating the shrinkage characteristics of mixtures was also discussed. The results show that a sand content of 30% is the minimum sand content for inhibiting the shrinkage behavior of bentonite–sand mixtures observed in this work: below this ratio, bentonite dominates the shrinkage process, and samples are prone to cracking due to uneven matrix suction; above this ratio, quartz sand forms a rigid skeleton that significantly inhibits volume shrinkage and accelerates water evaporation. Salt solutions suppress shrinkage by compressing the thickness of the diffuse double layer and inducing ion crystallization. Higher cation concentrations and valences (Mg²⁺ > Na⁺ > Ca²⁺) enhance the inhibitory effect. Crystalline salts such as Na₂SO₄ cause measurement deviations in water content due to hydration and delay the shrinkage process. However, NaCl solutions effectively inhibit shrinkage with minimal impact on shrinkage time. Fitting results with the MCG-B model (Coefficient of determination > 0.97) demonstrate that the MCG-B model can empirically describe the results of thin-film technique experiment, though the model’s prediction accuracy decreases for the residual shrinkage stage at high sand contents (>40%). This study provides a theoretical basis for optimizing buffer material proportions and curing processes, with significant implications for the long-term safety of HLW repositories. Full article

The increasing demand for sustainable construction materials has motivated the optimization of mortar mix designs to reduce cement consumption and its environmental impact while maintaining adequate mechanical performance. This study develops a machine learning (ML) model for optimizing mortar mixtures used in concrete roofing tiles by integrating aggregate particle packing techniques with non-linear regression algorithms, using an industry-grade dataset generated in the Central Laboratory of Wienerberger Ltd. Unlike most previous studies, which mainly focus on compressive strength, this research targets the transverse strength of industrial roof tile mortar. The proposed approach combines Tarantula Curve gradation limits, experimentally derived packing density ($\eta$), and ML regression within a unified and application-oriented workflow, representing a research direction rarely explored in the literature for optimizing concrete mix transverse strength. Fine concrete aggregates were characterized through a sand sieve analysis and subsequently adjusted according to the Tarantula Curve method to optimize packing density and minimize void content. Physical properties of cements and fine aggregates were assessed, and granulometric mixtures were evaluated using computational methods to calculate fineness modulus summation (FMS) and packing density. Mortar samples were tested for transverse strength at 1, 7, and 28 days using a three-point bending test, generating a robust dataset for modeling training. Three ML models—Random Forest Regressor (RFR), XG-Boost Regressor (XGBR), and Support Vector Regressor (SVR)—were evaluated, confirming their ability to capture nonlinear relationships between mix parameters and transverse strength. The analysis of input variables, which consistently ranked as the highest contributors according to impurity-based and permutation-based importance metrics, revealed that the duration of curing, density, and the summation of the fineness modulus significantly influenced the estimated transverse strength derived from the models. The integration of particle size distribution optimization and ML demonstrates a viable pathway for reducing cement content, lowering costs, and achieving sustainable mortar mix designs in the tile manufacturing industry. Full article

The cement and concrete industry is one of the primary sources of anthropogenic carbon dioxide (CO₂) emissions globally, responsible for nearly 8% of total emissions, making the need for a low-carbon transition urgent. CO₂ curing provides both strength enhancement and carbon sequestration, yet the compressive strength of such concrete remains challenging to predict due to limited and strongly coupled experimental factors. This study developed an explainable Automated Machine Learning (AutoML) framework with integrated uncertainty quantification to predict the 28-day compressive strength of CO₂-cured concrete. The framework was built using 198 standardized experimental data and trained with four algorithms—Random Forest (RF), Support Vector Regression (SVR), eXtreme Gradient Boosting (XGBoost), and the transformer-based Tabular Prior-Data Fitted Network (TabPFN). To enhance model accuracy and efficiency, stratified cross-validation, hyperparameter optimization, and bootstrap-based uncertainty analysis were applied during training. The results show that TabPFN achieves the highest predictive accuracy (test R² = 0.959) and maintains a stable 95% prediction interval. SHapley Additive exPlanations (SHAP) indicates that cement content, aggregate composition, water–binder (W/B) ratio, and CO₂ curing time are the dominant factors, with an optimal W/B ratio near 0.40. Interaction analysis further reveals synergistic effects between cement content and W/B, and a strengthening coupling between curing time and CO₂ concentration at longer durations. The framework enhances predictive reliability and explainability, supporting mixture design and curing optimization for low-carbon concrete development. Full article

When determining subgroups with heterogeneous treatment effects in cancer clinical trials, the threshold of a variable that defines subgroups is often pre-determined by physicians based on their experience, and the optimality of the threshold is not well studied, particularly when the mixture cure rate model is considered. We propose a mixture cure model that allows optimal subgroups to be estimated for both the time to event for uncured subjects and the cure status. We develop a smoothed maximum likelihood method for the estimation of model parameters. An extensive simulation study shows that the proposed smoothed maximum likelihood method provides accurate estimates. Finally, the proposed mixture cure model is applied to a colon cancer study to evaluate the potential differences in the treatment effect of levamisole plus fluorouracil therapy versus levamisole alone therapy between younger and older patients. The model suggests that the difference in the treatment effect on the time to cancer recurrence for uncured patients is significant between patients younger than 67 and patients older than 67, and the younger patient group benefits more from the combined therapy than the older patient group. Full article

This study investigates the settlement characteristics of overlying strata in backfilled stopes at the Sanshandao Gold Mine, focusing on the compaction behavior of backfill materials. Integrating laboratory tests, numerical modeling, and field monitoring, we analyzed the particle size distribution and fractal dimensions of tailings (2.1525) and C material (2.1994), with tailings showing better gradation. Systematic compaction tests examined the effects of mix ratio, water content, and curing time. Results indicate that compression follows a viscous sliding model with exponential curves, progressing through three stages—pore compaction, structural deformation, and elastic/plastic deformation—with energy dissipation ratios of 1:5:18. Water content was the most influential factor, with optimal compaction occurring at 5~8%. Coupled Midas-Flac3D simulations estimated a theoretical compaction rate of 0~2% in filled stopes, excluding seepage and equipment effects. Field monitoring at the −480 m level revealed non-uniform settlement, with maximum subsidence of 63.75 mm above stopes and initial settlement rates of 12~20 mm/month. At the −520 m mining level, the backfill compaction rate reached 0.31%, with minor future increases expected. These findings offer valuable guidance for backfill mixture design and strata control in mining engineering. Full article

This study evaluates the mechanical performance and predictive modeling of fine-grained soils stabilized with crushed aggregate residue (CAR) or crushed limestone waste (CLW) and Portland cement by integrating the porosity–binder index ($\eta /C_{\mathrm{i}\mathrm{v}}$) and Machine Learning (ML) techniques. Laboratory testing included unconfined compressive strength (q_u) and small-strain shear modulus (G_o) measurements on mixtures containing 15% and 30% CAR and 3% and 6% cement, compacted at dry unit weights between 1.69 and 1.81 g·cm⁻³ and cured for 7 and 28 days. Results revealed that strength and stiffness increased significantly with both cement and CAR contents. The mixture with 30% CAR and 6% cement exhibited the highest mechanical performance at 28 days (q_u = 1550 kPa and G_o = 6790 MPa). When mixtures are compared within the same curing period, the role of CAR and cement becomes evident. At 28 days, increasing CAR from 15% to 30% led to a moderate rise in q_u (from 1390 to 1550 kPa) and G_o (from 6220 to 6790 MPa). Likewise, at 7 days, increasing cement from 3% to 6% at 15% CAR produced significant gains in q_u (207 to 693 kPa) and G_o (2090 to 4120 MPa). The porosity–binder index showed strong correlations with q_u (R² = 0.94) and G_o (R² = 0.92). The ML models further improved accuracy, achieving R² values of 0.99 for q_u and 0.97 for G_o. Although the index already performed well, the additional gain provided by ML is meaningful because it reduces prediction errors and better captures nonlinear interactions among mixture variables. This results in more reliable estimates for mix design, confirming that the combined use of $\eta /C_{\mathrm{i}\mathrm{v}}$ and ML offers a robust framework for predicting the behavior of soil–cement–CAR mixtures. Full article

Accurate prediction of compressive strength in eco-friendly concretes, where part of the cement is replaced with recycled glass powder, remains a fundamental challenge for sustainable construction. This study evaluates and compares the performance of five machine learning models—Naïve Bayes, Random Forest, Decision Tree, Support Vector Machine (SVM), and k-Nearest Neighbors (k-NN)—for classifying the compressive strength of concretes with different mix designs and curing ages. The dataset includes 846 experimental samples produced at the School of Civil Engineering of UPM between 2004 and 2019. The results showed that Naïve Bayes and Random Forest achieved the highest accuracy and generalizability, confirming that the incorporation of glass powder does not introduce significant data instability and can serve as a viable and sustainable substitute of cement. The Decision Tree model provided the greatest interpretability, enabling insight into the influence of mixture parameters, while SVM and k-NN were primarily effective in extreme strength categories. Overall, the findings demonstrated that probabilistic and ensemble learning methods outperform deterministic and proximity-based algorithms in classifying materials with high compositional variability. This work reinforces the potential of artificial intelligence as a non-destructive, reliable, and scalable tool for optimizing the performance of low carbon concretes and promoting sustainable materials engineering. Full article

Penetration of harmful substances, such as chloride ions, is a major contributor to durability issues in concrete structures. Low permeability is critical for long-term performance, prompting the assessment and classification of concrete based on its resistance to ionic transport. However, the transport mechanisms are complicated and influenced by a range of interdependent factors including binder type, mixture proportions, specimen age, and curing conditions. There are two widely adopted test methods used for assessing chloride ion permeability: the Rapid Chloride Permeability Test (RCPT) and the Surface Resistivity Test (SRT), a non-destructive alternative. While RCPT is well-established, its long testing time as well as its high costs and sensitivity to specimen preparation limit its practicality. The SRT offers faster, more repeatable, and easier implementation. This state-of-the-art review systematically compares RCPT and SRT results across studies, revealing a strong inverse correlation with coefficients of determination (R²) from 0.85 to 0.95, as influenced by compressive strength, testing age, water-to-cement ratio, and supplementary cementitious material (SCM) type. Results showed that RCPT often has standard deviation (SD) values exceeding 300 coulombs and coefficient of variation (COV) values up to 10%, while SRT has lower variability (SD < 3 kΩ·cm and COV ≈ 5%). The review concludes that, with appropriate calibration, the SRT can reliably classify concrete permeability, closely aligning with RCPT results. However, research gaps remain regarding the applicability of existing models to less conventional SCMs and concrete types. Future research should prioritize the development of binder-specific correlations, validation using diffusion-based methods, and exploration of alternative SCMs and curing regimens to expand SRT applicability. Full article

The broader adoption of crumb rubber asphalt mixtures (CRAM) as sustainable pavement materials is currently limited by two key technical barriers. Firstly, there is a lack of standardized methods to evaluate mixing uniformity. Secondly, the material’s tendency for elastic recovery after compaction remains problematic. These barriers ultimately hinder the realization of CRAM’s full potential in vibration reduction, noise abatement, and resource recycling. To improve the performance evaluation system of CRAM and promote its development in engineering applications. Based on the distribution characteristics of crumb rubber in asphalt mixtures, this study established a crumb rubber distribution area moment model. It proposed a coefficient of area–distance variation to evaluate the mixing uniformity of CRAM. Through compaction tests and orthogonal tests, the effects of mixing process, mixing time, mixing temperature, compaction temperature, compaction times, and compaction method on the mixing uniformity and performance of CRAM are systematically investigated. The results show that, compared with specimens prepared by single compaction and compaction after high-temperature curing, CRAM specimens prepared by secondary compaction exhibit superior mechanical performance. The 24 h elastic recovery rate of these specimens is reduced to 24% of that in single-compacted specimens. The mixing process and mixing time have a significant impact on the mixing uniformity of CRAM. Pre-mixing crumb rubber with aggregates or extending the mixing time can improve the CRAM mixing uniformity by 45% and 18%, respectively. The mixing and compaction temperatures primarily affect the bulk density and Marshall stability of the specimens. When the mixing and compaction temperatures are 180 °C and 170 °C, respectively, the bulk density and Marshall stability of the molded specimens reach their maximum values. Through orthogonal analysis, the optimal mixing method for CRAM is determined as follows: mix aggregates and crumb rubber at 180 °C for 40 s, then add asphalt and continue mixing for another 80 s. The optimal process for secondary compaction is as follows: the first compaction at 170 °C, compacting each side 47 times, and the second compaction at 80 °C, compacting each side 23 times. Full article

Predicting unconfined compressive strength (UCS) is essential for the safety and stability of solid waste-based backfill materials, particularly due to the correlation between strength development and hazardous substance immobilization. This study developed a machine learning model to predict UCS and optimize mixtures using fly ash, slag, and desulfurized gypsum. A dataset with 14 input features—including composition, water content, and curing time—was analyzed using Recursive Feature Elimination (RFE) for feature selection. Random Forest, Bayesian, and Gray Wolf Optimizer (GWO)-enhanced models were compared. The GWO-GB model achieved superior accuracy (R² = 0.9335), with curing time (27.99%), water content (22.16%), and sulfur trioxide (18.98%) identified as the most significant features. The model enables rapid, high-precision UCS prediction, reduces experimental workload, and offers insights for mix design optimization and feature interaction analysis. Full article

The development of sustainable concrete capable of trading off the mechanical performance and cost remains a persistent scientific and engineering challenge. Although previous research has employed multi-objective optimization for binary and ternary cement blends, the simultaneous optimization of quaternary-blended systems, incorporating multiple supplementary cementitious materials, has received little systematic attention. This study addresses this gap by introducing an interpretable artificial intelligence (AI)-driven approach that integrates the Category Boosting (CatBoost) algorithm with the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to model and optimize the compressive strength (CS) and total cost of quaternary-blended concretes. A curated database of 810 experimentally documented mixtures was used to train and validate the model. CatBoost achieved superior predictive performance (R² = 0.987, MAE = 1.574 MPa), while Shapley additive explanations identified curing age, water-to-binder ratio, and Portland cement content as the dominant parameters governing CS. Multi-objective optimization produced Pareto-optimal elite mixtures achieving CS of 51–80 MPa, with a representative 60 MPa mix requiring approximately 62% less cement than conventional designs. The findings establish a scientifically grounded, interpretable methodology for data-driven design of low-carbon, high-performance concretes and demonstrate, for the first time, the viability of AI-assisted multi-criteria optimization for complex quaternary-blended systems. This framework offers both methodological innovation and practical guidance for implementing sustainable construction materials. Full article

The rheological behavior and low-temperature curing kinetics of poly(ethylene glycol fumarate)–acrylic acid systems initiated by benzoyl peroxide/N,N-dimethylaniline have been investigated for the first time with a focus on the development of thermosetting binders with controllable properties. It has been established that both composition and temperature have a significant effect on rheological behavior and kinetic parameters. Rheological studies revealed non-Newtonian flow behavior and thixotropic properties, while oscillatory tests demonstrated structural transformations during curing. Increasing the temperature was found to accelerate gelation, whereas a higher polyester content retarded the process, which is crucial for controlling the pot life of the reactive mixture. DSC analysis indicated that isothermal curing at 30–40 °C can be satisfactorily described by the Kamal autocatalytic model, whereas at 20 °C, at later stages, and at higher polyester contents, diffusion control becomes significant. The thermal behavior of cured systems was investigated using thermogravimetry. Calculations using the isoconversional Kissinger–Akahira–Sunose and Friedman methods confirmed an increase in the apparent activation energy for thermal decomposition, suggesting a stabilizing effect of poly(ethylene glycol fumarate) in the polymer structure. The studied systems are characterized by controllable kinetics, tunable viscosity, and high thermal stability, making them promising thermosetting binders for applications in composites, construction, paints and coatings, and adhesives. Full article

This study investigates the early-age compressive strength development of concrete incorporating ground granulated blast-furnace slag (GGBFS) under varying water-to-binder (W/B) ratios (35%, 45%, and 55%) and curing temperatures (5 °C, 20 °C, and 35 °C). Concrete mixtures were prepared with 0%, 20%, and 40% GGBFS replacement levels, maintaining a constant slump of 180 mm. The influence of GGBFS on fresh properties was evident, as higher GGBFS content reduced the demand for high-performance air-entraining water-reducing admixture (AEWR) by up to 72% at 40% GGBFS and W/B of 35%. All mixtures maintained target air content within 4.5 ± 1.5%. The Nurse–Saul maturity method was applied to determine the datum temperature $T_0$ (The minimum temperature required for the degree of maturity to increase) for early-age strength prediction. The optimal $T_0$ was found to be −3 °C for both OPC and GGBFS-blended concretes, replacing the conventional −10 °C value. Compressive strength predictions were conducted using Plowman, Logistic, and Gompertz models within the 5–10 MPa range. The Plowman and Gompertz models predicted early-age compressive strength with an error of approximately 10% in the 5–10 MPa range. In the lower strength range of 3–5 MPa, the Gompertz model exhibited superior predictive performance, with prediction errors 0.5–1 MPa lower than those obtained using the Plowman model. These findings will help in enhancing the maturity method’s reliability for low-temperature or time-constrained construction and support the use of GGBFS as a sustainable cement replacement. The study offers practical insights into optimizing early-age performance in blended cementitious systems. Full article

The curing of silicone networks from dimethylsilanediol and methylsilanetriol chainbuilder–crosslinker precursor mixtures is investigated from combined quantum/molecular mechanics simulations. Upon screening different crosslinker content from 5 to 15%, we provide a series of atomic-resolution bulk models all featuring 98–99% curing degree, albeit at rather different arrangement of the chains and nodes, respectively. To elucidate the nm scale alignment of the polymer networks, we bridge scales from atomic simulation cells to graph theory and demonstrate the analyses of 3-dimensional percolation of -O-Si-O- bonds, polydimethylsiloxane branching characteristics and the interpenetration of loops. Our findings are discussed in the context of the available experimental data to relate heat of formation, curing degree and elastic properties to the molecular scale structural details—thus promoting the in-depth understanding of silicone resins. Full article

To investigate the regulatory mechanism of polyoxymethylene (POM) fiber on the workability and mechanical properties of C30-grade coral aggregate concrete (CAC), this study designed six groups of CAC specimens with varying POM fiber volume fractions (0%, 0.2%, 0.4%, 0.6%, 0.8%, and 1.0%). Cube compressive test, axial compressive test, split tensile test, and flexural tests of CAC specimens after 28 days of curing were conducted, while observing their failure modes under ultimate load and stress–strain curves. The experimental results indicate that POM fiber incorporation significantly reduced the slump and slump flow of the CAC mixtures. The cube compressive strength, axial compressive strength, split tensile strength, and flexural strength of CAC initially increased and then decreased with increasing POM fiber volume fraction, peaking at 0.6% fiber content. Compared to the fiber-free group, these properties improved by 14.78%, 15.50%, 17.01%, 46.13%, and 3.69%, respectively. Analysis of failure modes under ultimate load revealed that POM fibers effectively reduced crack quantity and main crack width, producing a favorable bridging effect that promoted a transition from brittle fracture to ductile failure. However, when fiber volume fraction exceeded 0.8%, fiber agglomeration led to diminished mechanical performance. Based on experimental data, the constitutive relationship established using the Carreira and Chu model achieved a goodness-of-fit exceeding 0.99 for CAC stress–strain curves, effectively predicting mechanical behavior and providing theoretical support for marine engineering applications of coral aggregate concrete. This study provides a theoretical basis for exploiting coral aggregates as low-carbon resources, promoting CAC application in marine engineering, and leveraging POM fibers’ reinforcement of CAC to reduce reliance on high-carbon cement. Combined with coral aggregates’ local availability (cutting transportation emissions), it offers a technical pathway for marine engineering material preparation. Full article