Search Results (2,008)

Biochar, a carbon-rich material produced through the pyrolysis of wood residues and agricultural byproducts, has carbon storage capacity and potential as a low-carbon construction material. This study predicts the compressive strength of cementitious composites in which cement is partially replaced with biochar using machine learning models. A total of 716 data samples were analyzed, including 480 experimental measurements and 236 literature-derived values. Input variables included the water-to-cement ratio (W/C), biochar content, cement, sand, aggregate, silica fume, blast furnace slag, superplasticizer, and curing conditions. Predictive performance was evaluated using Multiple Linear Regression (MLR), Elastic Net Regression (ENR), Support Vector Regression (SVR), and Gradient Boosting Machine (GBM), with GBM showing the highest accuracy. Further optimization was conducted using XGBoost, Light Gradient-Boosting Machine (LightGBM), CatBoost, and NGBoost with GridSearchCV and Optuna. LightGBM achieved the best predictive performance (mean absolute error (MAE) = 3.3258, root mean squared error (RMSE) = 4.6673, mean absolute percentage error (MAPE) = 11.19%, and R² = 0.8271). SHAP analysis identified the W/C and cement content as dominant predictors, with fresh water curing and blast furnace slag also exerting strong influence. These results support the potential of biochar as a partial cement replacement in low-carbon construction material. Full article

Within natural rock masses, discontinuous joints are more prevalent than continuous joints. Discontinuous joints refer to non-persistent structural planes separated by intact rock bridges and can be quantified by the continuity coefficient K_A. They significantly affect the macroscopic mechanical properties of rock masses. Therefore, investigating discontinuous jointed rock masses with diverse morphologies carries considerable theoretical and engineering significance. Using 3D printing technology, resin-based specimens with discontinuous joints were subjected to laboratory mechanical tests to explore the evolution of failure mechanisms and mechanical properties of discontinuous jointed rock masses with different inclinations, undulation amplitudes, and structural plane continuity. Results show that under compression, discontinuous jointed rock masses consistently undergo combined tensile and shear stresses, with joint undulation amplitude and continuity governing coplanar crack initiation. As the joint inclination angle ranges from 0° to 90°, the peak compressive strength first decreases and then increases: specimens with continuous joints or discontinuous joints (continuity coefficient K_A < 0.25) follow a “V”-shaped trend, while those with K_A > 0.25 exhibit a “U”-shaped trend. Joint continuity is a key factor governing rock mass strength: at the same rock column radius, higher continuity results in lower strength, and vice versa. Joint morphology also influences strength, with specimens with regular zigzag joints and rectangular corrugated joints exhibiting 6.7% and 11.2% higher strength than smooth-jointed specimens, respectively. These results clarify the effects of joint continuity and undulation on rock mass strength, providing a theoretical foundation for the rapid determination of K_A via borehole imaging and laser scanning in engineering practice, and enabling direct prediction of rock mass strength trends. Full article

Vehicle repeated passes over soft terrain alter the soil’s bearing and shear behavior, thereby affecting vehicle mobility and energy consumption. To address this issue, this study conducted cyclic compression and shear tests on beach sand with moisture contents of 5%, 15%, and 25%. A constitutive model incorporating the coupling effects of loading cycles (N) and moisture content (ω) was developed based on the Bekker and Janosi model framework. The model expresses compression parameters as functions of N and ω, and describes shear behavior through the strength evolution function k(N,ω) and deformation modulus function h(N,ω). Results show excellent agreement between the model predictions and experimental data (R² > 0.92). Furthermore, a vehicle–soil coupled dynamics model was established based on the proposed constitutive model, forming a comprehensive analytical framework that integrates soil meso-mechanics with full vehicle–terrain interaction. This work provides valuable theoretical and technical support for predicting vehicle trafficability on coastal soft soils and optimizing vehicle suspension systems. Full article

This work explores the mechanical responses of 3D-printed periodic arch-inspired structures (PASs) and PASs doped with NdFeB powder to advance their application in lightweight structural load-bearing and future structure–function integration. Three PAS configurations were fabricated via digital light processing (DLP), and magnetic PASs (MPASs) were produced by dispersing NdFeB powder (1–3 g/200 mL) into photosensitive resin. Under quasi-static compression, key mechanical properties—Young’s modulus (E), yield strength (σ_y), and compressive strength (σ_c)—of non-magnetic PASs increase linearly with relative density (ρ* = 0.18–0.48): for PAS22, E rises from 68.1 to 200.3 MPa (+194%), σ_y from 2.18 to 6.75 MPa (+210%), and σ_c from 2.98 to 9.07 MPa (+204%). Under dynamic impact (~100 s⁻¹), mechanical enhancement is even more pronounced: E of PAS22 surges to 814.8 MPa (3.2× higher than quasi-static), and σ_c reaches 11.54 MPa. Finite element simulations reveal that the Ideal Plastic Model best predicts quasi-static brittle fracture, whereas the Hardening Function Model captures dynamic behavior most accurately. Stress and plastic strain concentrate at the straight–arc junctions—identified as critical weak points. MPASs exhibit higher stiffness and yield strength (e.g., E of MPAS22 up to 896.5 MPa under impact) but lower compressive strength (e.g., 11.01 MPa vs. 11.54 MPa for NMPAS22), attributed to NdFeB-induced brittleness that shifts the failure mode from “local damage accumulation” to “rapid overall failure”. This study establishes quantitative doping–structure–property correlations, providing design guidelines for next-generation functional arch-inspired metamaterials toward magnetically responsive, load-bearing applications. Full article

To investigate the complex functional relationships between pH, effective porosity, and compressive strength of planted concrete and their corresponding mixing ratios, a comprehensive database was developed from the relevant published literature. In this study, four machine learning (ML) algorithms were employed: a single algorithm—Multi-Layer Perceptron (MLP), and three ensemble algorithms—Gradient Boosting Regression (GBR), Extreme Gradient Boosting (XGBoost), and Random Forest Regression (RFR)—to predict the pH, effective porosity, and compressive strength of planted concrete. Additionally, the interpretable algorithm Shapley Additive Explanations (SHAP) was used to evaluate both global and local interpretations independent of the ML algorithms, providing insight into the decision-making process. The results demonstrate that the RFR algorithm achieved the highest R² values of 0.93 (pH), 0.97 (effective porosity), and 0.94 (compressive strength) in predicting planted concrete properties, demonstrating optimal predictive performance. Furthermore, cement content was identified as the most influential factor affecting pH, while design porosity and maximum coarse aggregate size were the primary factors influencing effective porosity, in that order. For compressive strength, the two most critical factors were the water reducer and cement content. Full article

In accelerated bridge construction, precast concrete girders are connected to cast-in-place concrete slab using shear transfer reinforcement across the interface plane to ensure the composite action. The steel transverse reinforcement is prone to severe corrosion due to the extensive use of de-icing salts and severe environmental conditions. As glass fiber-reinforced polymer (GFRP) reinforcement has shown to be an effective alternative to conventional steel rebars as flexural and shear reinforcement, the present research work is exploring the performance of GFRP reinforcements as shear transfer reinforcement between precast and cast-in-place concretes. Experimental testing was carried out on forty large-scale push-off specimens. Each specimen consists of two L-shaped concrete blocks cast at different times, cold joints, where GFRP reinforcement was used as shear friction reinforcement across the interface with no special treatment applied to the concrete surface at the interface. The investigated parameters included the GFRP reinforcement shape (stirrups and headed bars), reinforcement ratio, axial stiffness, and the concrete compressive strength. The relative slip, reinforcement strain, ultimate strength, and failure modes were reported. The test results showed the effectiveness and competitive shear transfer performance of GFRP compared to steel rebars. A shear friction model for predicting the shear capacity of as-cast, cold concrete joints reinforced by GFRP reinforcement is introduced. Full article

Non-destructive evaluation techniques are increasingly recognised as effective alternatives to destructive testing for estimating the compressive strength of lightweight aggregate concrete (LWAC). Among these, ultrasonic pulse velocity (UPV) is a well-established and widely employed method, characterised by its speed, non-invasiveness, and relative simplicity of implementation. In this study, an experimental dataset comprising 640 core segments from 160 cylindrical specimens, provided for analysis, was investigated. Each segment was described by physical and processing variables or features, including lightweight aggregate (LWA) and concrete densities, casting and vibration times, experimental dry density, and P-wave velocity obtained through UPV testing. A segregation index, derived from UPV measurements and defined as the ratio of local to mean P-wave velocity within each specimen, was also considered, following approaches previously suggested in the literature. A range of machine learning techniques was applied to assess the predictive capacity of local P-wave velocity and segregation index. Most ensemble-based methods and support vector regression (SVR) achieved the highest predictive performance when the segregation index was excluded, suggesting that its inclusion did not improve the predictive ability of the models. By contrast, Gaussian process regression (GPR) showed slight improvements when the segregation index was included. The results confirmed that the P-wave velocity measured by UPV testing is a reliable non-destructive predictor of compressive strength in LWAC. At the same time, the added value of the segregation index remained negligible under conditions of low segregation, as reflected by segregation index values above 0.8. These findings highlight the practical potential of integrating UPV-based measurements with data-driven modelling to enhance the reliability of concrete characterisation and quality control. Full article

Resistance against water penetration is one of the key indicators of concrete durability in humid and pressurized environments. An intelligent model based on the XGBoost machine-learning algorithm was developed to predict the water penetration depth, using 1512 independent experimental measurements. The influential variables included water pressure, pressure duration, thermal cycles, fiber content, curing, and compressive strength. The investigated concrete specimens and field-tested structures in this study were exposed to arid and hot climatic conditions, and the proposed model was developed within this environmental context. To accurately simulate the water transport behavior, a cylindrical-chamber test was employed, enabling non-destructive and in-situ evaluation of structures. Correlation analysis revealed that compressive strength had the strongest negative influence (r = −0.598), while free curing exhibited the strongest positive influence (r = +0.654) on penetration depth. After hyperparameter optimization, the XGBoost model achieved the best performance (R² = 0.956, RMSE = 1.08 mm, MAE = 0.81 mm). Feature importance analysis indicated that penetration volume, pressure, and curing were the most significant predictors. According to the partial dependence analysis, both pressure and duration exhibited an approximately linear increase in penetration depth, while a W/C ratio below 0.45 and curing markedly reduced permeability. Microstructural interpretation using MIP, XRD, and SEM tests supported the physical interpretation of the trends identified by the machine-learning model. The results demonstrate that machine-learning-models can serve as fast and accurate tools for assessing durability and predicting permeability under severe environmental conditions. Finally, the permeability of several real structures was evaluated using the machine-learning approach, showing excellent prediction accuracy. Full article

This study employed machine learning to investigate the mechanical behavior of one-part geopolymer (OPG)-stabilized soil subjected to acid erosion. Based on the unconfined compressive strength (UCS) data of acid-eroded OPG-stabilized soil, eight machine learning models, namely, Adaptive Boosting (AdaBoost), Decision Tree (DT), Extra Trees (ET), Gradient Boosting (GB), Light Gradient Boosting Machine (LightGBM), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost), along with hyper-parameter optimization by Genetic Algorithm (GA), were used to predict the degradation of the UCS of OPG-stabilized soils under different durations of acid erosion. The results showed that GA-SVM (R² = 0.9960, MAE = 0.0289) and GA-XGBoost (R² = 0.9961, MAE = 0.0282) achieved the highest prediction accuracy. SHAP analysis further revealed that solution pH was the dominant factor influencing UCS, followed by the FA/GGBFS ratio, acid-erosion duration, and finally, acid type. The 2D PDP combined with SEM images showed that the microstructure of samples eroded by HNO₃ was marginally denser than that of samples eroded by H₂SO₄, yielding a slightly higher UCS. At an FA/GGBFS ratio of 0.25, abundant silica and hydration products formed a dense matrix and markedly improved acid resistance. Further increases in FA content reduced hydration products and caused a sharp drop in UCS. Extending the erosion period from 0 to 120 days and decreasing the pH from 4 to 2 enlarged the pore network and diminished hydration products, resulting in the greatest UCS reduction. The results of the study provide a new idea for applying the ML model in geoengineering to predict the UCS performance of geopolymer-stabilized soils under acidic erosion. Full article

The Cerchar Abrasivity Index (CAI) is essential for predicting tool wear in mechanized tunneling and mining, but direct measurement requires time-consuming laboratory procedures. We developed a data-driven framework to estimate CAI from standard geomechanical and mineralogical properties using 193 rock samples covering igneous, metamorphic, and sedimentary lithologies. After evaluating 278 feature combinations with multicollinearity constraints (VIF < 10.0), we identified an optimal four-variable subset: brittleness index B₁, density, Equivalent Quartz Content (EQC), and Uniaxial Compressive Strength (UCS), with rock type indicators. CatBoost achieved the best performance (Test R² = 0.907, RMSE = 0.420), and SHAP analysis confirmed that density and EQC are primary drivers of abrasivity. Additionally, symbolic regression derived an explicit formula using only three variables (density, EQC, B₁) without rock type classification (Test R² = 0.720). The proposed framework offers a practical approach for assessing rock abrasivity at early project stages. Full article

Fibres can markedly enhance the uniaxial compressive strength (UCS) of cemented paste backfill (CPB). However, previous studies have mainly verified the effectiveness of polypropylene and straw fibres in improving the UCS of CPB experimentally, while systematic multi-factor evaluation remains limited. In this study, laboratory experiments were conducted on polypropylene- and straw fibre-reinforced CPB to construct a reliable dataset. The factors influencing the intensity of uniaxial compressive strength were divided into four aspects (mixture proportions, physical properties of the cement–tailings mixture, chemical characteristics of tailings, and fibre properties), and four intelligent models were developed for effectiveness analysis and UCS prediction. SHapley Additive exPlanations (SHAP) were employed to quantify the contributions of individual features, and the findings were experimentally validated. The GWO–LGBM model outperformed the SVR, ANN, and LGBM models, achieving R² = 0.907, RMSE = 0.78, MAE = 0.515, and MAPE = 0.157 for the training set, and R² = 0.949, RMSE = 0.627, MAE = 0.38, and MAPE = 0.115 for the testing set, respectively. Feature analysis reveals that mixture proportions contribute the most to UCS, followed by the tailings’ physical properties, the fibre properties, and the tailings’ chemical characteristics. This study found that cement content and tailings gradation control CPB structural compactness and fibres enhance bonding between hydration products and tailings aggregates, while the chemical composition of the tailings plays an inert role, functioning mainly as an aggregate. Full article

Fiber-reinforced polymers (FRPs) are being increasingly used to replace rebars as reinforcements for concrete. This study evaluated the seismic behavior of concrete walls reinforced with grid-type carbon FRP (CFRP; carbon grid) through quasi-static cyclic tests and compared the results with that of the reinforced concrete (RC) wall. The experimental variables were the ratio of the carbon-grid anchorage length in the foundation to the wall length and the axial force ratio. Based on the results of the quasi-static cyclic tests, the ratio of the equivalent stiffness at the crushing of the compression-edge cover concrete to the initial stiffness of the carbon-grid-reinforced concrete specimens was 0.14 on average. This indicates that the specimens reached their maximum load due to the crushing of the compression-edge cover concrete after a significant reduction in stiffness due to cracking. The skeleton curve for the carbon-grid-reinforced concrete specimens was found to be bilinear, with reduced stiffness due to cracking and failure due to the crushing of the compression-edge cover concrete, making it definable and predictable. Additionally, in specimens with a high axial force or small ratio of the anchorage length in the foundation to the wall length, some of the longitudinal CFRP strands fractured at the same time as they reached the failure load. Moreover, the load at the crushing of the compression-edge cover concrete of the carbon-grid-reinforced concrete specimen increased by 1.10 times with the increase in the axial force ratio and decreased by 0.96 times with the decrease in the ratio of the anchorage length in the foundation to the wall length. It was found to be 0.73–0.80 times the flexural strength based on the assumption of plane sections remaining plane. In comparison with RC specimen, the cumulative absorbed energy of the carbon-grid-reinforced concrete specimen began to decrease after a story drift ratio of 1%, and the cumulative absorbed energy up to the target story drift ratio of 3.0% was found to be 0.60–0.62 times that of the RC specimen. Full article

Accurate prediction of the mechanical and rheological properties of self-compacting concrete (SCC) is critical for mixture design and engineering decision-making; however, conventional empirical approaches often struggle to capture the coupled nonlinear relationships among mixture variables. To address this challenge, this study develops an integrated and interpretable hybrid machine learning (ML) framework by coupling three ML models (RF, XGBoost, and SVR) with five metaheuristic optimizers (SSA, PSO, GWO, GA, and WOA), and by incorporating SHAP and partial dependence (PDP) analyses for explainability. Two SCC datasets with nine mixture parameters are used to predict 28-day compressive strength (CS) and slump flow (SF). The results show that SSA provides the most stable hyperparameter optimization, and the best-performing SSA–RF model achieves test R² values of 0.967 for CS and 0.958 for SF, with RMSE values of 2.295 and 23.068, respectively. Feature importance analysis indicates that the top five variables contribute more than 80% of the predictive information for both targets. Using only these dominant features, a simplified SSA–RF model reduces computation time from 7.3 s to 5.9 s and from 9.7 s to 6.1 s for the two datasets, respectively, while maintaining engineering-level prediction accuracy, and the SHAP and PDP analyses provide transparent feature-level explanations and verify that the learned relationships are physically consistent with SCC mixture-design principles, thereby increasing the reliability and practical applicability of the proposed framework. Overall, the proposed framework delivers accurate prediction, transparent interpretation, and practical guidance for SCC mixture optimization. Full article

Al–Al₄C₃ composites exhibit promising mechanical properties including high specific strength, high specific stiffness. However, high reinforcement contents often promote brittle behavior, making it necessary to understand the mechanisms governing their limited toughness. In this work, a microstructural and mechanical study was carried out to evaluate the energy storage capacity in Al–Al₄C₃ composites fabricated by mechanical milling followed by heat treatment using X-ray diffraction (XRD) and Convolutional Multiple Whole Profile (CMWP) fitting method, the microstructural parameters governing the initial stored energy after fabrication were determined: dislocation density (ρ), dislocation character (q), and effective outer cut-off radius (R_e). Compression tests were carried out to quantify the elastic energy stored during loading (Es). The energy absorption efficiency (EAE) in the elastic region of the stress–strain curve was evaluated with respect to the elastic energy density per unit volume stored (Ee), obtained from microstructural parameters (ρ, q, and Re) present in the samples after fabrication and determined by XRD. A predictive model is proposed that expresses Es as a function of Ee and q, where the parameter q is critical for achieving quantitative agreement between both energy states. In general, samples with high EAE exhibited microstructures dominated by screw-character dislocations. High-resolution transmission electron microscopy (HRTEM) analyses revealed graphite regions near Al₄C₃ nanorods—formed during prolonged sintering—which, together with the thermal mismatch between Al and graphite during cooling, promote the formation of screw dislocations, their dissociation into extended partials, and the development of stacking faults. These mechanisms enhance the redistribution of stored energy and contribute to improved toughness of the composite. Full article

Long-term settlement of dredger fill presents substantial challenges to infrastructure stability, particularly in coastal areas such as Tianjin Nangang, where liquefied natural gas (LNG) pipelines are vulnerable to deformation caused by differential settlements. This study investigates the geological properties and long-term settlement characteristics of dredger fill in the Tianjin Nangang coastal zone and develops a simplified predictive model for long-term settlement. Comprehensive laboratory analyses, including field emission scanning electron microscopy (FESEM), X-ray diffraction (XRD) and mercury intrusion porosimetry (MIP), revealed a porous, flaky microstructure dominated by quartz and calcite, with mesopores (0.03–0.8 µm) constituting over 80% of total pore volume. A centrifuge modelling test conducted at 70 g acceleration simulated accelerated settlement behavior, demonstrating that approximately 70% of settlements occured within the initial year. The study proposes an enhanced hyperbolic model for long-term settlement prediction, which shows excellent correlation with experimental results. The findings underscore the high compressibility and low shear strength of dredger fill, highlighting the necessity for specific mitigation measures to ensure infrastructure integrity. This research establishes a simplified yet reliable methodology for settlement estimation, providing valuable practical guidance for coastal land reclamation projects. Full article