Asian Journal of Civil Engineering最新文献

Construction project scheduling involves complex trade-offs between time, cost, and quality (TCQ), often under conditions of uncertainty. This paper presents a novel approach using a fuzzy multi-objective particle swarm optimization (Fuzzy-MOPSO) algorithm to address the TCQ optimization problem in uncertain environments. By integrating fuzzy set theory with MOPSO, the model accommodates imprecise data and stakeholder preferences, allowing for a more realistic representation of construction project dynamics. Three objective functions are considered: minimizing project completion time (PCT), minimizing project construction cost (PCC), and maximizing project quality index (PQI). A case study involving a real-world construction project is employed to demonstrate the effectiveness of the proposed methodology. Twenty-six Pareto-optimal solutions were obtained and analyzed through trade-off plots and correlation analysis. The performance of the Fuzzy-MOPSO algorithm is benchmarked against other popular multi-objective optimization techniques, including NSGA-III, MODE, and MOTLBO. Results show that the proposed algorithm outperforms existing methods in convergence, diversity, and solution quality, achieving a more balanced TCQ optimization. The findings suggest that Fuzzy-MOPSO is a robust and efficient tool for construction managers seeking optimal schedules under uncertainty, contributing to better decision-making and resource allocation in complex project environments.

Modern seismic design codes prioritize structural resilience by accommodating inelastic deformation, especially in steel and reinforced concrete (RC) buildings. While this approach allows for ductile behaviour under strong earthquakes, it often results in structural damage. To reduce such damage, passive energy dissipation systems are increasingly utilized. This study introduces an innovative base isolation technique for RC frame structures, employing a hybrid U-shaped damper that integrates steel, shape memory alloy (SMA), and a rubber core. The U-shaped components, made from steel and SMA, exhibit elastoplastic and super elastic behaviour, respectively, enhancing energy absorption and self-centring capabilities. The rubber core, modelled using the hyper elastic Ogden formulation, contributes flexible isolation and nonlinear damping. Positioned at the base, the hybrid system enhances seismic performance by combining the isolating properties of rubber with the damping and recentring benefits of steel-SMA elements. Nonlinear time history analyses using four earthquake records confirmed that the hybrid isolator significantly reduces structural responses compared to systems without it.

This research examines the seismic performance of imperfections in multi-story steel-framed buildings. The structural behavior of 4, 6, and 8-story edifices was analyzed under seismic stress utilizing ETABS software and data from the Halabjah, Northridge, and Kobe earthquakes. Stories, roof displacement, and strain responses were among the variables studied by nonlinear dynamic and pushover analyses. Steel bracing configurations (central core, external corner, and exterior side) significantly enhanced earthquake resistance by augmenting lateral stiffness, reducing displacement, and controlling drift. The optimal design identified was center-core bracing, which decreased displacement by 52.32% and drift by up to 59.99%. The reduced manufacture of plastic hinges was an additional advantage of bracing, enhancing energy dissipation and structural resilience. The study emphasizes the impact of building height, earthquake intensity, and irregularity on seismic performance. The benefits were more apparent in shorter edifices; nevertheless, optimum bracing methods were essential for taller structures due to heightened flexibility and lateral force requirements. These findings underscore the significance of steel bracing in seismic design and retrofitting approaches, emphasizing its need in reinforced concrete structures in earthquake-prone regions.

Modern seismic design codes and standards (e.g., ASCE 7–16, Eurocode 8, NEHRP, RPA, 2024) prescribe the use of the pseudo-acceleration (PSA) spectrum to determine seismic loads and forces. These codes provide spectral acceleration values for various structural periods to ensure buildings are designed to withstand earthquake forces. PSA is derived from the displacement response of a system, offering a theoretical approximation of seismic forces rather than a direct measurement of ground acceleration. Instead, it is computed based on the relationship between response displacement and natural frequency. In contrast, relative acceleration more accurately represents the actual motion of a structure during an earthquake, making it a more physically intuitive measure of seismic forces. However, PSA approximations introduce significant errors. This study analyzes acceleration and displacement response spectra for single-degree-of-freedom systems using a carefully curated dataset from the Pacific Earthquake Engineering Research (PEER) ground motion database across various site periods. Pseudo-response spectra were derived from horizontal displacement spectral ordinates, and differences between actual and pseudo-response spectra were examined. Based on this analysis, correction formulas were developed to improve PSA accuracy. The key findings of this study can be summarized as that the discrepancy between real and pseudo-acceleration spectra is significantly larger for near-fault motions than for far-fault records. Therefore, a conversion model that effectively adjusts PSA spectra, accounting for both distance and magnitude is proposed. Finally, the model’s validity is confirmed within the parameter limits of the used database.

Any nation's progress is determined by the expansion and innovation of its infrastructure. In order to guarantee both structural efficiency and aesthetic appeal, the growing demand for high-rise structures in metropolitan settings calls for creative approaches to structural design. In high-rise building, voronoi, which are renowned for their capacity to produce organic, irregular patterns, provide a fresh framework for allocating loads and maximising material consumption. The performance of Voronoi-patterned steel frames under a range of load circumstances, including seismic and wind, is thoroughly examined using SAP2000 and parametric modelling in accordance with IS: 1893:2016 and IS: 875, respectively. Comparisons with conventional grid systems demonstrate how Voronoi patterns may result in lower material use and higher efficiency. According to the results, Voronoi grids are very advantageous for maximising the structural performance and visual attractiveness of tall steel buildings. This study lays the groundwork for future investigations into intricate geometric patterns in architectural design, indicating that Voronoi-based designs may eventually result in more inventive and sustainable skyscraper building.

Conventional modelling methods have been criticised to lack adequate applicability in characterising complex, nonlinear, uncertain associations between diffraction parameters and the phase composition of the modified concrete system. The purpose of this work is to conduct a study in which a framework based on machine learning can identify the phase in the bentonite calcite modified concrete based on X-ray diffraction (XRD) data. Concrete mixtures of different proportions of Ground Granulated Blast Furnace Slag (GGBS), bentonite and calcite were prepared, cured and tested to evaluate their compressive strength and the ideal mixture was chosen upon which further analysis was carried out. Concerning this mix, the 2θ-intensity profiles obtained by the X-ray diffraction data were taken to train and test two kernel-based regression models: Support Vector Regression (SVR) and Gaussian Process Regression (GPR), employing 80:20 ratio as the splitting line in the whole X-ray diffraction data. SVR showed good general capability because it successfully applied to high-dimensional data learning by optimizing on a margin. GPR conversely involved using a probabilistic kernel in order to establish latent nonlinearities and uncertainty on the data. The predictive performance was high in both models, where SVR had R² of 0.978 and 0.977 on training and testing respectively, and GPR had a R² of 0.982 and 0.979 on training and testing respectively. Also, the values of mean squared error confirmed the superiority of GPR. The results confirm the promising nature of machine learning, specifically SVR and GPR, as scalable and fully competent alternatives to established techniques in phase quantification, which are reliable and competent in capturing the microstructure, and there upon offers sensible guidelines in optimizing the microstructure of novel cementitious composites.

Choosing the right types and amounts of steel fibers and concrete mixtures is important for steel fiber reinforced concretes (SFRC) because they improve the flexural and compressive strength, making the concrete last longer. The variation in input factors, like the compositions of concrete mixes and steel fibers used, affects the unpredictable value of flexural strength. So, the study uses a set of experimental data to explore how steel fibers and different concrete mix compositions influence the flexural strength of SFRC. This study builds a prediction model for the flexural characteristics of SFRC using a database of 147 experimental results obtained from seventeen research groups. A Random Forest model creates an efficient prediction model and discovers key input features affecting flexural strength. The predictive flexural strength model is used along with Latin hypercube sampling (LHS) and a uniform probability distribution of input variables to create a large dataset. Then, the study used Sobol’s global sensitivity analysis method to investigate how different input factors affect the flexural strength of SFRC. The Sobol Index establishes and discusses the order in which input factors influence flexural strength. It is important information for selecting the optimal compositions of steel fiber-reinforced concrete.

Research has suggested substituting corrugated plates for flat plates to mitigate the adverse effects of buckling in these walls. However, corrugated plates typically result in a reduction in the maximum strength of the system. In this research, the characteristics of a single-layer hybrid system, which combines both flat and corrugated plates in the same plane, have been investigated in comparison to flat and fully corrugated SPSWs. This approach aims to reach the advantages of both plate types. The study employs the finite element method to investigate various configurations, with a focus on different corrugation locations, ratios, and angles. The modeling results indicate that the maximum strength of the hybrid system exceeds that of a fully corrugated wall and, in some cases, is comparable to the strength of a flat steel shear wall. Additionally, configurations with corrugation located in the middle of the hybrid system exhibit higher maximum strength than those with corrugation located elsewhere. The initial stiffness of the proposed hybrid system also exceeds that of traditional flat steel shear walls, with models featuring middle corrugation demonstrating even greater stiffness than fully corrugated shear walls. These findings underscore the efficacy of this new type of steel shear wall system.

Artificial intelligence has progressively revolutionized across different fields over the years, with particularly notable applications in dam engineering for monitoring and behavior prediction. Anomalies in these structures can lead to critical structural failures affecting stability and dam safety. For this reason, this review presents the contributions of artificial intelligence in dam monitoring for various applications, highlighting that many studies have been conducted on dams in China. A bibliometric study is included in this review to analyze the keywords network and the integration of artificial intelligence in civil engineering and dam monitoring by country, based on the last few years. This research focuses on machine learning models used to monitor deformations, cracks, seepage, and other anomalies encountered in structural health monitoring for dams. For training and testing these models, different methods and technologies are used for data collection, particularly sensors installed in dam structures, drone technology for image input, and the Building Information Modeling (BIM) method to facilitate relations among different participants and enable continuous monitoring of dam behavior. These models continue to develop, offering effective monitoring performance, although they require rich databases for optimal results. Additionally, artificial intelligence algorithms can be used for applications other than prediction and monitoring, such as data denoising and optimization. Multiple models can be combined to achieve comprehensive real-time dam monitoring with optimal results and high accuracy.

Graphical abstract

Sustainable retrofitting of infrastructure involves complex trade-offs between project duration, cost, and environmental impact. This study introduces a novel multi-objective optimization framework using the opposition-based learning multi-objective teaching–learning-based optimization (OBL-MOTLBO) algorithm. The proposed time-cost-environmental trade-off (TCET) model aims to minimize retrofitting time (RT), cost (RC), and carbon-equivalent emissions (REI) simultaneously. A case study conducted in the Delhi-NCR region spans eleven retrofitting domains, each with three intervention options. The OBL-MOTLBO algorithm outperforms established methods (e.g., NSGA-II, MOACO) in generating a diverse, converged Pareto front. To support decision-making, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied, allowing prioritization of solutions under different stakeholder preferences. Among 18 Pareto-optimal solutions, Solution 17 (RT = 98 days, RC = $295,000, REI = 320,000 kg CO₂-eq) ranks highest across all scenarios. The framework offers a robust, scalable method for sustainable retrofit planning, integrating economic and environmental objectives into data-driven decision-making.