Search Results (42)

Growing concerns about global climate change and its negative consequences for communities have put immense pressure on the building industry, which is one of the primary sources of greenhouse gas emissions. Due to the environmental issues associated with the manufacture of sustainable construction materials, alkali-activated concrete (AAC) has emerged as a competitive alternative to cement. To predict the compressive strength (CS) of AAC, four machine learning (ML) models, namely, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), were employed in this study using 193 data points. The input variables include Precursor “P” (kg/m³), Blast Furnace Slag “BFS ratio”, Sodium hydroxide “Na” (kg/m³), silicate modulus “Ms”, water content “W” (kg/m³), fine aggregate “FA” (kg/m³), coarse aggregate “A” (kg/m³), and curing time “CT” (day), with CS (MPa) as the output variable. The dataset was checked for stationarity and then normalized to decrease data redundancy and increase integrity. Furthermore, three model combinations were developed based on the relationship between the input and target variables. The XGB-M3 model outperformed all other models with a high degree of accuracy, according to the study’s findings. Specifically, the Pearson correlation coefficient (PCC) was 0.9577, and the mean absolute percentage error (MAPE) was 14.95% during the calibration phase. SHAP, an explainable AI approach that provides interpretable insights into complex AI systems by assigning feature importance to model predictions, was employed. Results suggest the higher predictions from the XGB-M3 and RF-M3 models were largely driven by curing time (CT). Full article

Pozzolanic concrete demonstrates intricate, highly nonlinear material interactions that pose significant challenges for the accurate prediction of compressive strength (CS). This study introduces a novel, interpretable ensemble machine learning (ML) framework for predicting CS based on 759 mixture records encompassing cement, aggregates, supplementary cementitious materials (pozzolans), water/binder (W/B), superplasticizer, water, and curing age. Descriptive analysis and ANOVA were used to identify key predictors, followed by an 80/20 train–test split with 10-fold cross-validation to ensure robust and generalizable modeling. To further enhance model reliability, 5% of outliers were removed using an isolation forest algorithm, after which data were normalized and ensemble hyperparameters optimized. Among the evaluated models, the extra trees algorithm with standard scaling demonstrated the most stable generalization, achieving a coefficient of determination (R²) of 0.978 and a root mean square error (RMSE) of 4.197 MPa on the test set, and R² = 0.966 (RMSE = 5.053 MPa) under 10-fold cross-validation. Feature importance, SHAP, and partial dependence analyses consistently demonstrated that W/B, curing age, and cement are the principal determinants of CS. Finally, multi-objective optimization generated high-strength, low-impact mixtures, confirming the framework’s effectiveness as a transparent decision-support tool for performance- and sustainability-oriented pozzolanic concrete design. This study is novel in combining interpretable ensemble ML with multi-objective optimization to simultaneously achieve precise CS prediction and the formulation of sustainable, performance-optimized pozzolanic concrete mixtures. Full article

Tight sandstone gas development is largely governed by mineral composition and micromechanical heterogeneity. This study proposes a cross-scale method integrating these two factors to characterize macroscopic sandstone heterogeneity. First, a CNN–Transformer model was trained on thin-section images to identify mineral types and contents. Second, probability density functions of Young’s modulus for each mineral were derived from nanoindentation data, and stochastic sampling was used to assign mechanical properties to mineral grains in an FDEM-GBM uniaxial compression model. Finally, numerical results validated against experiments show that the random spatial distribution of micromechanical parameters leads to a normal distribution of the macroscopic Young’s modulus. Decreasing high-strength mineral content reduces the mean Young’s modulus while increasing its standard deviation, indicating greater mechanical heterogeneity, with cracks preferentially propagating in low-strength minerals. Mineral composition and content are the primary controls on macroscopic behavior, while micromechanical heterogeneity plays a secondary role. A brittleness index integrating mineral composition and multi-scale Young’s modulus distribution is proposed, providing a theoretical basis for evaluating heterogeneity and fracability in tight sandstone reservoirs. Full article

Understanding the mechanical behavior of lunar regolith is critical for the success of future lunar excavation and construction missions. Irregular particle morphology and low geostatic stress are recognized as key factors contributing to the high internal friction angle of this unique extraterrestrial geomaterial. However, the underlying mechanisms by which low geostatic stress enhances shear strength remain unclear, and the multiscale effects of particle morphology on shear strength evolution are not yet fully elucidated. In this study, consolidated drained triaxial compression tests were performed on CUMT-1 lunar regolith simulant and Fujian standard sand to investigate their macroscopic mechanical behavior. Complementary discrete element simulations of biaxial compression were conducted to analyze mesoscopic mechanical responses of granular materials under the influence of multiscale particle morphology and confining stress. A robust macroscopic–mesoscopic strength correlation model was established, incorporating normalized mean interparticle contact force and mean coordination number to predict the normalized deviatoric stress of granular assemblies. Based on this model, the mesoscopic mechanisms through which irregular particle morphology and low geostatic stress enhance the internal friction angle were quantitatively investigated. The findings offer new insights into the shear strength characteristics of in situ lunar regolith and provide theoretical support for lunar surface construction and excavation operations. Full article

This study investigated the failure mechanism and load-bearing capacity of ultra-high-performance concrete (UHPC) columns confined with high-strength spiral stirrups under axial compression. Based on tests of 75 specimens, a structural stability analysis method was employed to convert multi-point strain measurements into the normalized generalized strain energy density (E_j,norm). The mutation point (Point U) on the E_j,norm-F_j curve, identified via the Mann–Kendall criterion, was proposed as a novel indicator for structural instability and the practical failure load. Parametric analysis showed that increasing the UHPC compressive strength from 100 MPa to 180 MPa raised the failure load by 63%, while increasing the stirrup volumetric ratio from 0.9% to 2.0% yields a further 7.5% increase in the failure load. In contrast, the yield strength of stirrups exerts a negligible influence on the failure load, as the stirrups do not reach their yield strength at the failure load of the concrete columns. A new predictive model for the failure load was developed, which exhibited excellent agreement with test results (mean ratio = 1.000, standard deviation = 0.046, errors within ±13%). The proposed method provided a reliable and stable approach for evaluating the failure load-bearing capacity of confined UHPC columns. The validated predictive model enabled engineers to determine the failure load of confined UHPC columns through simple calculation rather than expensive experimental testing, reducing project costs by 5–10% through optimized material selection and accelerating design timelines by weeks, thereby making UHPC columns more economically competitive for mainstream infrastructure applications. Full article

The growing need for sustainable and lightweight building materials has accelerated research on alternatives to conventional concretes, out of which Lightweight Expanded Clay Aggregate (LECA) concrete has emerged as a promising solution. However, the high porosity and nonlinear mechanical behavior of LECA concrete complicate the accurate prediction of compressive strength through conventional empirical models. The main focus of the paper is on identifying a comprehensive machine learning-based framework for modeling and predicting the 28-day compressive strength of LECA-based lightweight concrete. The dataset was created and preprocessed by using statistical normalization and correlation analysis. In this study, five supervised machine learning models—Multiple Linear Regression (MLR), Support Vector Regression (SVR), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost)—were developed and fine-tuned using a grid-search strategy combined with ten-fold cross-validation. The quality of the prediction made by each model was evaluated by means of standard performance indicators, such as the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). After the evaluation, the models were subsequently compared and ranked according to the Gray Relational Analysis (GRA) method. The comparative assessment shows that CatBoost demonstrated the most reliable performance, achieving an R² of 0.907, RMSE of 3.41 MPa, MAE of 2.47 MPa, and MAPE of 10.05%, outperforming the remaining algorithms. To interpret the significance of features, SHAP (Shapley Additive exPlanations) analysis was applied, which identified water and LECA content as the dominant factors influencing compressive strength, followed by the cement and fine aggregate proportions. The findings reveal that the ensemble-based gradient boosting model is capable of capturing intricate nonlinear interactions, as observed in the heterogeneous matrix of LECA concrete. Full article

This study provides computational insights into the flexural strengthening of reinforced concrete (RC) T-beams in the negative moment region using steel-reinforced polymer cement mortar (PCM) overlays. A validated three-dimensional nonlinear finite element (FE) model was developed using the Advanced Tool for Engineering Nonlinear Analysis (ATENA) software (version 2023.0.0.22492) to simulate the behavior of beams retrofitted with 40 mm thick PCM layers embedded with 13 mm and 16 mm deformed bars. Model validation was performed against previously published experimental results reported by the authors, demonstrating excellent agreement, with normalized mean square error (NMSE) values expressed as fractions between 0.0001 and 0.0022, and experimental-to-numerical ultimate load ratios ranging from 0.99 to 1.01. Parametric analyses were then conducted to investigate the influence of key variables, concrete compressive strength, PCM overlay thickness, and longitudinal reinforcement ratio on the global flexural performance. The results revealed that increasing the overlay thickness raised the ultimate load capacity by up to 15.4% and improved energy absorption by 43%. Enhancing concrete strength led to gains of up to 12.5% in load capacity and 15.8% in stiffness. Variations in reinforcement ratio had the most significant impact, increasing peak load by up to a factor of 2.02 and improving energy absorption by up to a factor of 1.49. Despite these improvements, reductions in ductility were observed across all strengthening configurations, underscoring a strength–deformability trade-off critical for seismic applications. These findings affirm the efficacy of steel-reinforced PCM overlays and provide design-oriented insights for optimizing negative moment retrofitting strategies in RC bridge girders and continuous beam systems. Full article

To address the lack of contact and breakage parameters in the discrete element method (DEM) simulation of potato seed cutting and sorting processes, this study took the ‘Atlantic’ potato seed as the research object and constructed a crushable potato model using EDEM. Through physical experiments, the mean average diameter, moisture content, density, Poisson’s ratio, and elastic modulus were measured. The coefficients of collision restitution, static friction, and rolling friction between the potato seed and the Q235 steel plate were determined as 0.576, 0.559, and 0.073, respectively. Using the actual repose angle of 22.89° as the response target, and combining the steepest ascent test with the Box–Behnken design, the non-cohesive contact parameters between potato seed particles were calibrated. The resulting coefficients of collision restitution, static friction, and rolling friction between particles were determined as 0.404, 0.412, and 0.0156, respectively. Finally, based on physical shear tests (maximum shear force 23.56 N), significant influencing factors were identified through Plackett–Burman screening as the bonding radius ratio r and the normal stiffness per unit area Kn. Using the steepest ascent test and the Box–Behnken response surface method, the key bonding parameters of the Bonding V2 model were calibrated as follows: r = 1.098, K_n = 8.597 × 10⁷ N·mm⁻³, tangential stiffness per unit area K_t = 3.250 × 10⁶ N·mm⁻³, critical compressive stress σ_n = 5.500 × 10⁵ Pa, and shear strength τ_t = 3.000 × 10⁴ Pa. The relative error between the simulated and actual maximum shear forces was 0.89%, which is small. The calibrated flexible model accurately represents the physical characteristics of potato seeds and provides a reliable reference for the design of mechanized potato seed cutting and sorting equipment. Full article

The SBPDN (Steel Bar Prestressed Deformed Normal relaxation) bar, which has ultra-high yield strength yet much lower bond resistance than conventional deformed bars, has been recently proposed to be used as the longitudinal rebar instead of a normal-strength deformed bar to simply realize strong earthquake-resilient concrete components. To facilitate and promote the application of concrete components reinforced with SBPDN rebars to the structures located in earthquake-prone regions, it is indispensable to develop reliable and effective anchoring means and clarify the bond strength of SBPDN bars embedded in concrete and/or grout mortar. This paper presents experimental information on the pull-out tests of fifteen SBPDN bars embedded in grout mortar, along with a discussion on the effective anchorage details and the bond strength of SBPDN bars. The tested SBPDN bars have a nominal diameter of 22.2 mm, the maximum diameter currently available on the market. All SBPDN bars were embedded in high-strength grout mortar with a targeted compressive strength of 60 MPa. The primary experimental variables included the end anchorage details, the diameter of sheath ducts, and the embedded length of the bars. Test results demonstrated that either screwing two nuts and a washer at the end of SBPDN bars or providing a rolling-threaded end region was effective in preventing them from premature slip from grout mortar. If the embedment length was 20 times the bar diameter or longer, the proposed two anchorages could ensure the SBPDN bar to fully develop its specific yielding strength as high as 1275 MPa. In addition, it has also been experimentally revealed that the bond strength of SBPDN bars embedded in grout mortar was much lower than that of conventional deformed bars and varied between 2.84 MPa and 3.98 MPa. Full article

This study presented a comprehensive finite element (FE) investigation into the flexural behavior of RC T-beams strengthened in the negative moment region using near-surface mounted (NSM) carbon-fiber-reinforced polymers (CFRP) rods. A three-dimensional nonlinear FE model was developed and validated against experimental data, achieving close agreement with normalized mean square error values as low as 0.006 and experimental-to-numerical ratios ranging from 0.95 to 1.04. The validated model was then employed to conduct a systematic parametric analysis considering CFRP rod diameter, concrete compressive strength, longitudinal reinforcement ratio, and FRP material type. The results showed that increasing CFRP diameter from 6 to 10 mm enhanced ultimate load by up to 47.51% and improved stiffness by 1.48 times. Higher concrete compressive strength contributed to stiffness gains exceeding 50.00%, although this improvement was accompanied by reductions in ductility. Beams with reinforcement ratios up to 2.90% achieved peak loads of 309.61 kN, but ductility declined. Comparison among FRP materials indicated that CFRP and AFRP offered superior strength and stiffness, whereas BFRP provided a more balanced combination of strength and deformation capacity. Full article

It is essential to elucidate the shear mechanical behavior of structural planes to assess the risk to rock masses and protect them from shear failure. Current research on shear mechanical behavior is focused on isotropic structural planes with the same lithology on both sides. However, anisotropic structural planes, commonly found in nature, may exhibit unique mechanical behavior that differs from isotropic structural planes. Therefore, it is necessary to study the factors affecting the shear strength of the anisotropic structural planes. In this paper, the direct shear numerical tests on anisotropic structural planes were carried out using the three-dimensional distinct element code (3DEC) based on the laboratory test. The numerical test results illustrate that the error between the peak shear strength of the numerical test and the laboratory test is basically within 10%. The shear stress-displacement curves of the numerical and laboratory tests are similar, which verifies the accuracy of the numerical test. According to the Barton standard sections, anisotropic structural plane models with different roughness and size were established, and the direct shear numerical tests with different normal stresses were carried out. To predict the peak shear strength of the anisotropic structural planes, one hundred and eighty-one sets of direct shear numerical test data were selected. Normal stress, roughness, compressive strength of soft and hard rock masses, basic friction angle of soft and hard rock masses, and structural plane size were used as input parameters to establish a back propagation (BP) neural network model. The research results show that, under identical conditions, the shear strength of the anisotropic structural planes decreases as the structural plane size increases. On the contrary, the shear strength increases with the increasing structural plane roughness and normal stress. For the BP neural network prediction model, the root mean square error (RMSE) and coefficient of determination (R²) of the training set are 0.441 and 0.957. For the test set, the RMSE is 0.489, and R² is 0.947, which indicates that the predicted values are in good agreement with the actual values. Full article

The effectiveness of repair work relies on whether the interface substrate can achieve sufficient bond strength when subjected to numerous stresses. This study investigated the bond properties of repaired normal concrete (NC-to-NC) elements, including cube, beam, and U-shaped specimens, after undergoing natural fracture due to flexural and tensile stresses. The specimens were repaired using a polyurethane (PU) matrix by gluing the two parts and applying compression, splitting, and drop-weight impact (DWI) tests to evaluate the bond strength properties. The results revealed that the PU matrix effectively repairs NC substrate with adequate bond strength, which exceeds the minimum allowable bond strength specified in the ASTM ACI 546-06 to rehabilitate damage concrete structures. The reference beams exhibit a peak applied load capacity of 15.6 kN with less deflection than the repaired samples. The compressive strength of the NC-to-NC repaired specimens loaded along and parallel to the interface plane revealed a decrease in compressive strength of 47.3% and 31.5% compared to the NC-R samples, respectively. The mean number of blows at the cracking stages appeared nearly equal for reference and repaired NC-to-NC specimens. The reference specimens exhibited an average number of 24 and 31 blows at the initial and failure stages, respectively, which were higher by 9.1% and 5.2% than the NC-to-NC repaired specimens. The PU binder showed promising results in achieving adequate interfacial bond strength under static and impact loads. Full article

Cement-stabilized rammed earth (CSRE) is a variation of the traditional rammed earth building material, which has been used since ancient times, strengthened by the addition of a stabilizer in the form of Portland cement. This article compares the compressive strength of CSRE determined from specimens cored from structural walls and those molded in the laboratory. Both types of specimens underwent a 120-day curing period. The tests were conducted on specimens with various grain sizes and cement content. An analysis of variance (ANOVA) was performed on the obtained results to determine whether it is possible to establish a conversion factor between the compressive strength values obtained from laboratory-molded cubic samples and those from cored samples extracted from the CSRE structure. The study revealed that the compressive strength of CSRE increases significantly over the curing period, with substantial strength gains observed up to 120 days. The results indicated no statistically significant difference in the mean unconfined compressive strength (UCS) between cubic and cored specimens for certain mixtures, suggesting that a shape coefficient factor may not be necessary for calculating CSRE compressive strength in laboratory settings. However, for other mixtures, normal distribution was not confirmed. These findings have implications for the standardization and practical application of CSRE in construction, highlighting the need for longer curing periods to achieve optimal strength and the potential to simplify testing protocols. Full article

A series of numerical true triaxial compression tests were carried out on rubber–sand mixtures (RSMs) by means of the 3D discrete element method to study the effect of the intermediate principal stress ratio b on the failure properties of RSMs with different rubber contents (RCs), and to explore the effect mechanism from a microscopic point of view. The numerical simulation results show that as the intermediate principal stress ratio $b$ increases and the peak deviator stress $q_{peak}$ gradually increases, while the peak internal friction angle ${\phi }_b$ first increases and then decreases. The numerical simulation results were compared with four common strength criteria, including the modified Lade–Duncan criterion, the SMP criterion, the FKZ criterion and the DP criterion. The comparative analysis showed that the existing common criteria cannot accurately predict the damage state of RSMs, suggesting the necessity for further research. At the micro level, the combined effects of the intermediate principal stress ratio $b$ values and RC on the micro-parameters, such as the coordination number, average normal stress between particles, probability density and anisotropy, were investigated. Full article

The objective of this study is to investigate the performance of normal- and high-strength concretes including limestone powder (LP) through their mechanical properties. Moreover, sustainable flat plates made of these concretes were investigated through their punching strength. For this purpose, two different types of concrete (normal- and high-strength) with various limestone replacement ratios of 0%, 5%, 15%, and 20% by weight were designed. The fresh and hardened characteristics of the mixtures were investigated at various ages. By this means, the experimental behavior of reinforced concrete (RC) flat plate slabs made with limestone powder subjected to punching shear failure was studied. Slump value increased up to a 5% replacement of LP; after that, there was a tendency for the slump value to decrease as the replacement of limestone in normal-strength LP concrete increased. However, slump values for high-strength LP concrete increased as the LP replacement amount increased. There was a steady decrease in the compressive strength and splitting tensile strength values with the increase in LP content in normal concrete. However, in the high-strength LP concrete, with more than 10% of replacement LP, a decrease in the compressive strength values and splitting tensile strength values occurred. Compared to the control slab specimen without LP, in normal strength, the slab specimens with LP exhibit a larger ultimate shear load for slab specimens containing 5% and 10% of LP. The maximum increment for RC slabs containing 10% limestone powder was 3.8%. However, in high-strength concrete, the slab specimens with LP remained at the same ultimate shear load as control slabs, up to 10% of LP. high-strength concrete slabs with 5–20% LP showed an overall increase of (17.2%) in punching strength over the corresponding LP normal-strength concrete slabs. The corresponding increase for control slabs was 18.8%. It can be concluded that introducing LP improves the slab punching strength in a similar way that is found in non-sustainable slabs when using either normal- or high-strength concrete. Full article