Search Results (569)

This study aims to enhance the performance of an innovative steel slag–ceramsite foam concrete (SSCFC) to advance sustainable green building materials. An eco-friendly composite construction material was developed by integrating industrial by-product steel slag (SS) with lightweight ceramsite. Employing a three-factor, three-level orthogonal experimental design at a fixed density of 800 kg/m³, 12 mix proportions (including a control group) were investigated with the variables of water-to-cement (W/C) ratio, steel slag replacement ratio, and ceramsite replacement ratio. The governing mechanisms of the W/C ratio, steel slag replacement level, and ceramsite replacement proportion on the SSCFC’s fluidity and compressive strength (CS) were elucidated. The synergistic application of range analysis and analysis of variance (ANOVA) quantified the significance of factors on target properties, and partial least squares regression (PLSR)-based prediction models were established. The test results indicated the following significance hierarchy: steel slag replacement > W/C ratio > ceramsite replacement for fluidity. In contrast, W/C ratio > ceramsite replacement > steel slag replacement governed the compressive strength. Verification showed R² values exceeding 65% for both fluidity and CS predictions versus experimental data, confirming model reliability. Multi-criteria optimization yielded optimal compressive performance and suitable fluidity at a W/C ratio of 0.4, 10% steel slag replacement, and 25% ceramsite replacement. Full article

Glass fiber-reinforced polymer (GFRP) composites exhibit significant advantages over conventional structural webbing materials, including lightweight and corrosion resistance. This study investigates the localized compression performance of the proposed GFRP grid web–concrete composite beam through experimental and numerical analyses. Three specimen groups with variable shear-span ratios (λ = 1.43, 1.77) and local stiffener specimens were designed to assess their localized compressive behavior. Experimental results reveal that a 19.2% reduction in shear-span ratio enhances ultimate load capacity by 22.93% and improves stiffness by 66.85%, with additional performance gains of 77.53% in strength and 94.29% in stiffness achieved through local stiffener implementation. In addition, finite element (FE) analysis demonstrated a strong correlation with experimental results, showing less than 5% deviation in ultimate load predictions while accurately predicting stress distributions and failure modes. FE parametric analysis showed that increasing the grid thickness and decreasing the grid spacing within a reasonable range can considerably enhance the localized compression performance. The proposed analytical model, based on Winkler elastic foundation theory, predicts ultimate compression capacities within 10% of both the experimental and numerical results. However, the GFRP grid strength adjustment factor β_g should be further refined through additional experiments and numerical analyses to improve reliability. Full article

Polypropylene (EPP) concrete offers advantages such as low density and good thermal insulation properties, but its relatively low strength limits its engineering applications. Waste steel fibers (WSFs) obtained during the sorting and processing of machining residues can be incorporated into EPP concrete (EC) to enhance its strength and toughness. Using the volume fractions of EPP and WSF as variables, specimens of EPP concrete (EC) and waste steel fiber-reinforced EPP concrete (WSFREC) were prepared and subjected to cube compressive strength tests, splitting tensile strength tests, and four-point flexural strength tests. The results indicate that EPP particles significantly improve the toughness of concrete but inevitably lead to a considerable reduction in strength. The incorporation of WSF substantially enhanced the splitting tensile strength and flexural strength of EC, with increases of at least 37.7% and 34.5%, respectively, while the improvement in cube compressive strength was relatively lower at only 23.6%. Scanning electron microscopy (SEM) observations of the interfacial transition zone (ITZ) and WSF surface morphology in WSFREC revealed that the addition of EPP particles introduces more defects in the concrete matrix. However, the inclusion of WSF promotes the formation of abundant hydration products on the fiber surface, mitigating matrix defects, improving the bond between WSF and the concrete matrix, effectively inhibiting crack propagation, and enhancing both the strength and toughness of the concrete. Full article

The simulation analysis of a novel stay-in-place formwork (SIPF) beam reinforced with perforated steel pipe skeleton was conducted. The SIPF beam consists of a modified magnesium oxysulfide mortar (MMOM) formwork, a square steel pipe skeleton with holes dug on the sides and top, and cast-in-place concrete. The finite element (FE) analysis model of the SIPF beam was established by using the ABAQUS CAE 2021 software, and simulation analysis was conducted with the shear span ratio (SSR), the distance between the remaining steel strips, and the strength of concrete as the variation parameters. The results show that the stiffness and shear capacity of the SIPF beam decrease with the increase in SSR and increase with the decrease in strip spacing. Under the same conditions, when the concrete strength grade is increased from C30 to C50, the shear bearing capacity of the SIPF beam increases by 11.8% to 16.2%. When the spacing of the steel strips is reduced from 200 mm to 150 mm, the shear bearing capacity can be increased by 12.7% to 31.5%. When the SSR increases from 1.5 to 3.0, the shear bearing capacity decreases by 26.9% to 37.3%. Moreover, with the increase in the SSR, the influence of the steel strip spacing on the shear bearing capacity of the SIPF beam improves, while the influence of the concrete strength on the shear bearing capacity decreases. Taking parameters such as SSR, steel strip spacing, and concrete strength as variables, the influence of steel pipe constraining the core concrete on the shear bearing capacity was considered. The calculation formula for the shear bearing capacity of the SIPF beam with perforated steel pipe skeleton was established. The calculation results fit well with the laboratory test and simulation test results and can be used for the design and calculation of engineering structures. Full article

This study investigates the influence of varying water-to-cement (W/C) ratios and fine aggregate compositions on the performance of concrete incorporating expanded perlite aggregate (EPA) as a lightweight alternative to natural sand. A total of eighteen concrete mixes were produced, each with different W/C ratios and fine-to-coarse aggregate (FA/CA) ratios, and evaluated for workability, compressive strength, flexural and tensile strength, water absorption, density, and thermal conductivity. Perlite was used to fully replace natural sand in half of the mixes, allowing a direct assessment of its effects across low-, medium-, and high-strength concrete formulations. The results demonstrate that EPA can improve workability and reduce both density and thermal conductivity, with variable impacts on mechanical performance depending on the W/C and FA/CA ratios. Notably, higher cement contents enhanced the internal curing effect of perlite, while lower-strength mixes experienced a reduction in compressive strength when perlite was used. These findings suggest that expanded perlite can be effectively applied in structural and non-structural concrete with optimized mix designs, supporting the development of lightweight, thermally efficient concretes. Mixture W16-100%EPS was considered the ideal mix because its compressive strength at the age of 65 days 44.2 MPa and the reduction in compressive strength compared to the reference mix 14% and the reduction in density 5.4% compared with the reference mix and the reduction in thermal conductivity 14% compared with the reference mix. Full article

This present study proposes different machine learning-based predictors for the assessment of the residual compressive strength of Self-Compacting Concrete (SCC) subjected to high temperatures. The investigation is based on several literature algorithmic approaches based on Artificial Neural Networks with distinct training algorithms (Bayesian Regularization, Levenberg–Marquardt, Scaled Conjugate Gradient, and Resilient Backpropagation), Support Vector Regression, and Random Forest methods. A training database of 150 experimental data points is derived from a careful literature review, incorporating temperature (20–800 °C), geometric ratio (height/diameter), and corresponding compressive strength values. A statistical analysis revealed complex non-linear relationships between variables, with strong negative correlation between temperature and strength and heteroscedastic data distribution, justifying the selection of advanced machine learning techniques. Feature engineering improved model performance through the incorporation of quadratic terms, interaction variables, and cyclic transformations. The Resilient Backpropagation algorithm demonstrated superior performance with the lowest prediction errors, followed by Bayesian Regularization. Support Vector Regression achieved competitive accuracy despite its simpler architecture. Experimental validation using specimens tested up to 800 °C showed a good reliability of the developed systems, with prediction errors ranging from 0.33% to 23.35% across different temperature ranges. Full article

In order to improve the durability of loess-based composite coal gangue porous planting concrete (LCPC), the effects of fly ash and slag powder content on the durability and microstructure of LCPC were studied. In this paper, fly ash and slag powder were mixed into LCPC, and freeze-thaw cycle and dry-wet cycle tests were carried out. The compressive strength, dynamic elastic modulus, and mass change were used as evaluation indices to determine the optimal mix ratio for LCPC durability. Scanning electron microscopy (SEM) was performed, and the experimental design was carried out with the water–cement ratio, fly ash, and slag powder content as variables. The microstructure characteristics of LCPC were analyzed. The results show that the maximum number of freeze-thaw cycles can reach 35 times and the maximum number of dry-wet cycles can reach 50 when 5% fly ash and 20% slag powder are used. With an increase in the water-cement ratio, the skeleton of the loess gradually became complete, and its structure became more compact. In the micro-morphology diagram, the mixed fly ash and slag powder particles are not obvious, but with an increase in dosage, the size of the cracks and pores gradually decreases. The incorporation of fly ash and slag powder can play a positive role in the durability of LCPC and improvement of its microstructure. The results of this study are crucial for improving the application performance of ecological restoration, soil improvement, and long-term stability of structures, and can provide a scientific basis for the sustainable development of environmentally friendly building materials. Full article

This study investigates two novel prefabricated frame joints: prestressed steel sleeve-connected prefabricated reinforced concrete joints (PSFRC) and non-prestressed steel sleeve-connected prefabricated reinforced concrete joints (SSFRC). A total of three PSFRC specimens, four SSFRC specimens, and one cast-in-place joint were designed and fabricated. Seismic performance tests were conducted using different end-plate thicknesses, grout strengths, stiffener configurations, and prestressing tendon configurations. The experimental results showed that all specimens experienced beam end failures, and three failure modes occurred: (1) failure of the end plate of the beam sleeve, (2) failure of the variable cross-section of the prefabricated beam, and (3) failure of prefabricated beams at the connection with the steel sleeves. The load-bearing capacity and initial stiffness of the structure are increased by 35.41% and 32.64%, respectively, by increasing the thickness of the end plate. Specimens utilizing C80 grout exhibited a 39.05% higher load capacity than those with lower-grade materials. Adding stiffening ribs improved the initial stiffness substantially. Specimen XF2 had 219.08% higher initial stiffness than XF1, confirming the efficacy of stiffeners in enhancing joint rigidity. The configuration of the prestressed tendons significantly influenced the load-bearing capacity. Specimen YL2 with symmetrical double tendon bundles demonstrated a 27.27% higher ultimate load capacity than specimen YL1 with single centrally placed tendon bundles. An analytical model to calculate the moment–rotation relationship was established following the evaluation criteria specified in Eurocode 3. The results demonstrated a good agreement, providing empirical references for practical engineering applications. Full article

To address the persistent challenges of substantial land occupation, intricate construction sequencing, and extended project timelines inherent to conventional substation accident oil sumps, this research introduces a novel integrally prefabricated circular cross-section oil containment structure. The study establishes a finite element representation of this prefabricated system to systematically examine structural deformation mechanisms and failure patterns under combined hydrostatic and geostatic loading scenarios. Through parametric analysis of the oil tank structure, the influences of longitudinal reinforcement diameter, thickness–diameter ratio, height–diameter ratio, and concrete-strength grade on the mechanical characteristics of the structure are explored. Utilizing the response surface methodology for the parametric optimization in finite element analysis, a comprehensive optimization of critical geometric design variables is conducted. These results indicate that longitudinal reinforcement diameter and concrete-strength grade exert negligible influence on concrete stress except for stress increase under internal pressure, with higher concrete grades. The thickness-to-diameter ratio dominantly regulates structural responses: response surface optimization achieved 12% stress reduction and 14% displacement mitigation at 220 mm wall thickness under internal pressure, despite a 4% stress increase under external loading. Height-dependent effects require specific optimization, with 18% stress reduction beyond 3000 mm under external pressure but 20% stress increase at 3400 mm under top loads. Geometric refinements enable 34–50% displacement reduction in critical zones, providing validated references for prefabricated oil tanks. Full article

The shear strength of rock discontinuities is a critical parameter in rock engineering projects for assessing the safety conditions of rock slopes or concrete dam foundations. It is primarily controlled by the frictional contribution of rock texture (basic friction angle), the roughness of discontinuities, and the applied normal stress. While proper testing is essential for accurately quantifying shear strength, engineering geologists and engineers often rely on published historical databases during early design stages or when test results show significant variability. This paper serves two main objectives. First, it intends to provide a comprehensive overview of the basic friction angle concept from early years until its emergence in the Barton criterion, along with insights into distinctions and misunderstandings between basic and residual friction angles. The other, given the influence of the basic friction angle for the entire rock joint shear strength, the manuscript offers an extended database of basic friction angle values. Full article

The drilling and blasting method is commonly employed for the rapid demolition of outdated buildings by destroying key structural components and inducing progressive collapse. The residual bearing capacity of these components is governed by the deformation morphology of the longitudinal reinforcement, characterized by bending deflection and exposed height. This study develops and validates a finite element (FE) model of a reinforced concrete (RC) column subjected to demolition blasting. By varying concrete compressive strength, the yield strength of longitudinal reinforcement, the longitudinal reinforcement ratio, and the shear reinforcement ratio, 45 FE models are established to simulate the post-blast morphology of longitudinal reinforcement. Two databases are created: one containing 45 original simulation cases, and an augmented version with 225 cases generated through data augmentation. To predict bending deflection and the exposed height of longitudinal reinforcement, artificial neural network (ANN) and random forest (RF) models are optimized using the hunter–prey optimization (HPO) algorithm. Results show that the HPO-optimized RF model trained on the augmented database achieves the best performance, with MSE, MAE, and R² values of 0.004, 0.041, and 0.931 on the training set, and 0.007, 0.057, and 0.865 on the testing set, respectively. Sensitivity analysis reveals that the yield strength of longitudinal reinforcement has the most significant impact, while the shear reinforcement ratio has the least influence on both output variables. The partial dependence plot (PDP) analysis indicates that the ratio of shear reinforcement has the most significant impact on the deformation of longitudinal reinforcement. Full article

Predicting the flexural behavior of fiber-reinforced ultra-high-performance concrete (UHPC) remains a significant challenge due to the complex interactions among numerous mix design parameters. This study presents a machine learning-based framework aimed at accurately estimating the modulus of rupture (MOR) of UHPC. A comprehensive dataset comprising 566 distinct mixtures, characterized by 41 compositional and fiber-related variables, was compiled. Seven regression models were trained and evaluated, with Random Forest, Extremely Randomized Trees, and XGBoost yielding coefficients of determination (R²) exceeding 0.84 on the test set. Feature importance was quantified using Shapley values, while partial dependence plots (PDPs) were employed to visualize both individual parameter effects and key interactions, notably between fiber factor, water-to-binder ratio, maximum aggregate size, and matrix compressive strength. To validate the predictive performance of the machine learning models, an independent experimental campaign was carried out comprising 26 UHPC mixtures designed with varying binder compositions—including supplementary cementitious materials such as fly ash, ground recycled glass, and calcium carbonate—and reinforced with mono-fiber (straight steel, hooked steel, and PVA) and hybrid-fiber systems. The best-performing models were integrated into a hybrid neural network, which achieved a validation accuracy of R² = 0.951 against this diverse experimental dataset, demonstrating robust generalizability across both material and reinforcement variations. The proposed framework offers a robust predictive tool to support the design of more sustainable UHPC formulations incorporating supplementary cementitious materials without compromising flexural performance. Full article

This article considers the effect of constant and variable Poisson’s ratio on cracking in concrete and rock specimens under uniaxial compression using mechanical systems modeling methods. The article presents an analysis of the data confirming the increase in Poisson’s ratio under specimen loading. A system of equations for modeling the effect of Poisson’s ratio on cracking under uniaxial compression is proposed. The comparison showed that the model with a constant Poisson’s ratio predicts a thickness of the surface layer with cracks that is underestimated by approximately 10%. In practice, this means that the model with a constant Poisson’s ratio underestimates the risk of failure. A technique for analyzing random deviations of Poisson’s ratio from the variable mathematical expectation is proposed. The comparison showed that the model with a variable Poisson’s ratio leads to results that are more cautious, i.e., it does not potentially overestimate the safety factor. The model predicts an increase in uniaxial compression strength when using external reinforcement. An equation is proposed for determining the required wall thickness of a conditional reinforcement shell depending on the axial compressive stress. The study contributes to understanding the potential vulnerability of load-bearing structures and makes a certain contribution to increasing their reliability. Full article

This paper investigates the potential of deep learning in predicting the compressive strength of ultra-high-performance concrete (UHPC) by developing a convolutional neural network (CNN) model with two convolutional layers. The proposed CNN architecture demonstrates capability in accurately predicting UHPC compressive strength from tabular data encompassing various material compositions. Ten input variables were selected, including the cement content, water content, silica fume content, silica powder content, silica sand content, superplasticizer content, and curing parameters. The model was trained and tested on a dataset comprising 219 samples. Experimental results indicated excellent predictive performance, with the CNN achieving a coefficient of determination (R²) of 0.959 on the test set and a mean absolute percentage error (MAPE) of 5.55%, demonstrating both accuracy and stability. Comparative analysis revealed that the CNN’s performance was comparable to established machine learning methods like XGBoost (R² = 0.961), which are typically more suited for tabular data. Furthermore, SHAP (SHapley Additive exPlanations) analysis confirmed the model’s interpretability. These findings collectively suggest that the CNN-based approach shows considerable promise for predicting compressive strength across diverse UHPC formulations. Full article

Bond behavior between steel bars and concrete is fundamental to the structural integrity and durability of reinforced concrete. However, corrosion-induced deterioration severely impairs bond performance, highlighting the need for advanced and reliable assessment methods. This paper pioneers an algorithm for an advanced ensemble learning framework to predict bond strength between corroded steel bars and concrete. In this framework, a novel Stacked Boosted Bond Model (SBBM) is developed, in which a Fusion-Based Feature Selection (FBFS) strategy is integrated to optimize input variables, and SHapley Additive exPlanations (SHAP) are employed to enhance interpretability. A merit of the framework is that it can effectively identify critical factors such as crack width, transverse confinement, and corrosion level, which have often been neglected by traditional models. The proposed SBBM achieves superior predictive performance, with a coefficient of determination (R²) of 0.94 and a mean absolute error (MAE) of 1.33 MPa. Compared to traditional machine learning and analytical models, it demonstrates enhanced accuracy, generalization, and interpretability. This paper provides a reliable and transparent tool for structural performance evaluation, service life prediction, and the design of strengthening measures for corroded reinforced concrete structures, contributing to safer and more durable concrete structures. Full article