Asian Journal of Civil Engineering最新文献

Nowadays, architectural requirements affect structural design investigations. On the other hand, the pounding effect is one of the crucial effects between two adjacent high-rise buildings under seismic load. Because shear walls experience higher stresses at their ends, end shear walls alleviate these stresses and enhance the effect of shear walls in high-rise buildings. This study aimed to evaluate the impact of end shear walls on the seismic pounding between two adjacent 20-story reinforced concrete buildings subjected to seven far-field seismic records by nonlinear time history analysis. Also, the distance between the two buildings is considered zero. The inclusion of end shear walls was found to significantly reduce seismic pounding effects. Specifically, notable reductions were observed in average pounding displacements and rotational accelerations in the horizontal (X) direction. Average pounding drifts in the X-direction decreased by up to 26%, while average pounding accelerations in the X-direction were reduced by up to 9%. Similarly, pounding accelerations in the vertical (Z) direction and vertical pounding rotations were also substantially reduced. These findings highlight the effectiveness of end shear walls in mitigating seismic pounding and improving the overall seismic performance of adjacent reinforced concrete high-rise buildings subjected to far-fault ground motions.

This research reports a laboratory study on the optimal levels of vitrified Polish waste (VPW) and ground granulated blast furnace slag (GGBS) as partial substitutes for cement to examine the strength properties of concrete. Ordinary Portland cement was partially substituted with 5%, 10%, 15%, and 20% mixtures of vitrified polish waste and ground granulated blast-furnace slag (GGBFS). The water-to-cementitious materials ratio was consistently set at 0.38 for all mixtures. The concrete’s strength qualities were assessed using compressive testing, strength testing, splitting tensile strength testing, and flexural strength testing. The compression strength test was executed at 7 and 28 days of curing, while the split tensile strength and flexural strength tests were conducted on M30, M35, and M40 grade concrete. The mix proportions for M30, M35, and M40 are 1:1.615:3.427, 1:1.50:3.25, and 1:1.40:3.15, respectively. The test findings demonstrated that the compressive strength, split tensile strength, and flexural strength of concrete mixtures incorporating GGBFS and VPW enhance with the increasing proportions of GGBS and VPW. A multilayer perceptron (MLP) neural network was used to evaluate concrete strength, and the predicted results were very similar to the actual measurements. The findings demonstrate that an optimal level of 15% GGBFS and VPW relative to the total binder content yields no further enhancement in compressive strength, split tensile strength, or flexural strength with additional GGBFS and VPW.

This study investigates the optimal design of counterfort retaining walls through the application of a Differential Evolution (DE) algorithm. A typical counterfort retaining wall comprises four fundamental components: stem, toe, heel, and counterfort. By treating the dimensions of these elements and the associated reinforcements as design variables, the optimal design process identifies the most cost-effective dimensions while adhering to the various constraints. The DE algorithm, a population-based optimization technique similar to Genetic Algorithms, distinguishes itself through its unique methodologies for crossover, mutation, and population updating. The construction cost of a retaining wall primarily encompasses the expenses for concrete, reinforcement steel, and formwork. In this study, the wall geometry was optimized using the DE algorithm, with the optimization framework implemented in MATLAB software. The computed results were compared with the recommended values for different wall heights. To ascertain the optimal combination of feasible design variables, objective functions were employed, contingent on the design variable values. This investigation utilized 12 design variables and 12 design constraints to optimize the objective function. Counterforts are incorporated to enhance the stability of the main wall, with a minimum thickness defined to ensure compliance with the specified lower limit values. Furthermore, the objective function was formulated for wall heights of 6, 7, 8, 9, and 10 m above ground level using the DE algorithm. The results demonstrate that the optimization of counterfort retaining walls can significantly reduce construction costs.

Anchor bolts of the column base are important in ensuring the stability and safety of pre-engineered steel frames. The reliability of anchor bolts is influenced by various random factors, including geometric dimensions, material properties, loads, and particularly the corrosion status. This study aims to evaluate the reliability of steel column anchor bolts in marine environments where metal corrosion is a dominant factor. A deterministic model for calculating the safety condition of anchor bolts is built and then developed into a stochastic model by considering geometric dimensions, material properties, loads, and corrosion status as random variables. The safety probability (reliability) of the anchor bolts is evaluated through Latin hypercube sampling and Monte Carlo simulation. The research results indicate that the safety probability of anchor bolts in a marine atmospheric environment tends to decrease over time. Specifically, for Model 1, the safety probability decreases from 94.26% after 10 years, 87.96% after 15 years, 63.66% after 25 years, and only 6.78% after 50 years. Model 2 exhibits a slower decline, with the safety probability decreasing from 96.2% after 10 years to 92.4% after 15 years, 80.46% after 25 years, and 33.25% after 50 years. Meanwhile, Model 3 shows a higher probability of maintaining safety, with a likelihood of decreasing from 96.82% after 10 years, 94.11% after 15 years, 86.29% after 25 years, and 52.14% after 50 years. Although the structure met the safety requirements according to the initial model, the results of the random analysis showed that the risk of damage increased due to the influence of random variables, especially metal corrosion in the marine environment.

Concrete, as the most extensively used construction material, contributes significantly to environmental degradation due to the high consumption of natural resources and carbon dioxide emissions. To foster sustainable development, this study investigates the incorporation of alternative materials Fly Ash and Rice Husk Ash as partial replacements for cement in M25 grade concrete. The research evaluates both the compressive strength and workability of these modified mixes. Furthermore, machine learning techniques, including XGBoost, Random Forest, and Support Vector Machine (SVM), were employed to predict the compressive strength based on experimental data. A user-friendly prediction system was developed to enable analysis by selecting either Fly Ash or Rice Husk Ash as the replacement material. Among the models used, XGBoost outperformed the others in terms of predictive accuracy, achieving the highest (hbox {R}^{2}) score and lowest error metrics. The results indicate that these alternative materials can enhance concrete properties at specific replacement levels, and that machine learning models, particularly XGBoost, offer accurate and efficient predictions. This study underscores the potential of integrating sustainable materials with data-driven modeling for eco-friendly and performance-optimized concrete mix designs.

A numerical investigation was carried out for both cases of cracked and uncracked reinforced (RC) and fibrous reinforced (FRC) /unfibrous concrete specimens incorporating recycled aggregate (RA), which were assessed to explore the failure properties of these materials, using the “Pi Distribution Method” (PDM). PDM is based on the non-random distribution of aggregates, which depends on the falling aggregate experiment. A finite element (FE) software (ABAQUS) is used to model the concrete specimens. The results demonstrate an improvement in the mechanical properties of concrete after adding RA. The incorporation of RA into the concrete results in an increase in elasticity and greater displacement at loading due to their important ductility, high porosity, and weak bond to the cement matrix. RA contributes to enhancing post-cracking behavior and stress redistribution. The elastic behavior of RA concrete shows a more horizontal load-displacement curve, indicating its deformability. Both concrete reinforcements, by bars and fibers, are analyzed in terms of Load-Displacement and load-CMOD (Crack Mouth Opening Displacement) for the notched specimen. The addition of steel fibers to RA contributes to the increase of the linear elastic stiffness before and at the maximal force of the specimens by acting as dispersed reinforcement. The efficiency of the proposed numerical FE mesoscopic model based on PDM is confirmed for all the study cases. This study opens new paths in the literature, explores many types of recycled aggregates and various fiber types, and considers other structural elements as beams, columns, and joints.

This research is groundbreaking in its combination of machine learning and finite-element modeling to assess M30-grade concrete mixtures, which include 53-grade Ordinary Portland cement, ground granulated blast-furnace slag, and basalt fiber, all at a water-to-cement ratio of 0.35. Sixteen different mix designs were evaluated for their compressive strength and corrosion characteristics. Tests on 150 mm cubes revealed that Sample 10 was the best, reaching a compressive strength of 36.5 MPa after 28 days with a displacement of 0.013 mm. Corrosion was measured in a 3.5% NaCl solution using a four-electrode macrocell setup, with simulations conducted via COMSOL Multiphysics. Machine learning models such as random forest (RF), extreme gradient boosting (XGBoost), and support vector regression (SVR) were employed to predict compressive strength and corrosion metrics. RF demonstrated the highest accuracy 0.401–0.704 V, 4.50 × 10⁻⁷-1.65 × 10⁻⁵ A cm^-2). XGBoost (MAE: 0.4–0.5, R²: 0.90) and SVR (MAE: 0.55–0.7, R²: 0.83) showed moderate and lower accuracy, respectively. This integrated RF-FEM approach offers high predictive accuracy. It also presents a novel framework that combines mechanical and corrosion modeling in SCM-modified concrete.

This study aims to identify important factors, analyze them through the Analytical Hierarchy Process (AHP), Consistent Fuzzy Preference Relations (CFPR), and Survey, and predict the possibility of successful implementation of the Internet of Things (IoT) and Cloud Computing (CC) in the construction sector of Gujarat, India. Previous studies primarily focused on ranking factors through various Multi Criteria Decision Making (MCDM) methods, but this research uniquely predicts success/failure. From past studies, twenty important factors were identified, and a questionnaire survey, along with personal interviews, collected one hundred twenty responses from construction experts in Gujarat, India. The responses were analyzed using AHP, CFPR, and Survey to identify important factors and predict success/failure. The findings show that the most important factor is cost and connectivity issues, with priority weights of 0.1644 (AHP), 0.0836 (CFPR), and 0.0530 (Survey) and predicted success/failure weights of AHP (0.7465/0.2535), CFPR (0.7473/0.2527), and Survey (0.7418/0.2582). According to the prediction values, the possibility of success is twice that of failure, indicating that IoT and CC can be successfully implemented in the construction sector of Gujarat, India. The findings of this study can guide decisions on implementation, predict success/failure, aid in future planning, determine necessary improvements, and evaluate associated risks and benefits. These findings have broad applicability and can be used to implement IoT and CC within the construction sector globally.

Bricks are fundamental materials extensively utilized in masonry construction. However, the conventional production of clay bricks necessitates substantial extraction of natural clay, leading to environmental degradation and unsustainable land use. To mitigate these issues, the incorporation of alternative materials as partial substitutes for clay is imperative. In this regard, Pond Ash (PA), a by-product of thermal power plants, emerges as a viable replacement when blended with Cement and Stone Dust (SD), offering a sustainable solution for brick manufacturing without compromising essential mechanical and physical properties. This study investigates the application of Machine Learning (ML) algorithms for predicting the 28-day Compressive Strength (CS) of PA-based bricks reinforced with Cement and SD. The experimental mix design maintained a constant PA content of 70%, while the remaining 30% consisted of varying proportions of Cement and SD. The predictive modeling was based on an experimental dataset comprising 100 samples, with mixture proportions as input features and CS as the target variable. To capture the nonlinear and complex relationships inherent in the dataset, four supervised regression models were employed: Random Forest (RF), Gradient Boosting Regressor (GBR), AdaBoost Regressor (ADA), and a Stacked Ensemble (SE) model. Model performance was rigorously evaluated using the coefficient of determination (R²) and validated against a separate dataset of 8 unseen experimental values. Additionally, a 10-fold cross-validation strategy was implemented to ensure model generalizability and minimize overfitting. The R² values obtained for RF, GBR, ADA, and SE were 0.989, 0.989, 0.982, and 0.989, respectively, indicating a high degree of accuracy and consistency in strength prediction. These results underscore the effectiveness of ML-based approaches in modeling the compressive behavior of PA-based bricks. The integration of ML techniques into the analysis and design process demonstrates significant potential in optimizing sustainable brick formulations, thereby contributing to the advancement of eco-efficient construction practices. Microstructural studies also have been carried out.

The goal of the present work is to propose a novel bi-level optimization framework that simultaneously considers vibration serviceability at the upper level along with fire resistance performance at the lower level in an effort to minimize the floor depth while keeping integrity sets to both objectives. Most existing studies in this area have either used deterministic or single-objective optimization techniques, which are incapable of recognizing uncertainty and multi-scale interactions in real-life scenarios of fire vibration in process. These approaches often fall short in their ability to encapsulate material behavior which is stochastic in nature, the topological degradation, and the coupling behavior across scales, particularly when considered in the light of two hazard conditions. In this context, five innovative computational strategies are envisaged in process. The Uncertainty Infused Pareto Front Propagation (UPFP) captures stochastic variability in material and loading parameters to create robust Pareto fronts. The Graph-Coupled Fire Vibration Topology Optimizer (GCFVTO) models geometry- and physics-couplings through a dynamic graph. Deep Surrogate-Assisted Multi-Fidelity Optimization (DSAMFO) means to deploy deep Gaussian process surrogates for real-time design evaluation to make it computationally cheaper. Multi-Scale Serviceability-Safety Coupled Simulator (MS3CS) connects microstructural degradation with modal performance across scales. Ultimately, the Game-Theoretic Dual-Level Decision Optimizer (GTDLDO) provides a strategic equilibrium setting for counterbalancing conflicting objectives, utilizing Stackelberg game theory during implementation. These methods make together a computationally reliable, physically consistent, and uncertainty-aware optimization framework. This work may provide entirely new avenues towards robust and multi-objective decision-making within the performance-based floor system design.