Asian Journal of Civil Engineering最新文献

This study explores the optimization of reinforced cellular lightweight concrete (RCLC) beams under cyclic loading by integrating sustainable materials and advanced modelling techniques. Cement was partially replaced with limestone powder, and natural fine aggregates with recycled construction and demolition waste (CDW), to generate six concrete mixes. Mechanical behaviour was assessed using non-destructive tests (Ultrasonic Pulse Velocity and Rebound Hammer), along with flexural strength evaluation over 28 days. Results showed that moderate replacement levels, particularly in Mix N4, delivered optimal mechanical performance and internal uniformity. Furthermore, an Artificial Neural Network (ANN) model was developed using MATLAB to predict mechanical properties based on mix parameters. The model demonstrated strong generalization ability with a low mean squared error, proving its reliability for performance forecasting. This research supports sustainable construction by promoting waste reuse, minimizing carbon emissions, and validating machine learning techniques for material optimization.

Modern seismic design of steel structures demands innovative approaches that optimize material strength while maintaining ductility and energy dissipation capacity. Introducing web openings into standard rolled sections, resulting in castellated and cellular beams, has emerged as an effective strategy to achieve these goals. By reducing self-weight and creating efficient load paths, these beams offer potential gains in structural performance under earthquake loading. This study examines the seismic response of a G + 9 steel moment-resisting frame configured with conventional, castellated, and cellular beams. Response Spectrum Analysis (RSA) is performed in ETABS software in accordance with IS 1893 (Part 1): 2016 provisions. Key response metrics such as lateral displacement, story drift, base shear, and time period are compared across the three beam configurations. Results indicate that both castellated and cellular beams outperform conventional sections: lateral displacements decrease by up to 37%, and story drifts reduce by up to 34%. Correspondingly, base shear values drop by up to 26.8%, signifying improved energy dissipation characteristics. The time period increases by approximately 40–42% for sections containing web openings, reflecting a trade-off between stiffness and flexibility. While these findings are promising, they are limited to linear dynamic analysis and idealized configurations. Overall, this research confirms that integrating castellated and cellular beams into steel frames can yield effective and economical improvements in seismic resilience.

This study presents a systematic data-driven approach for predicting the compressive strength (CS) of fly ash-based geopolymer mortars using three statistical modeling techniques Linear Regression (LR), Multiple Linear Regression (MLR), and Nonlinear Regression (NLR). The primary objective is to address the inherent complexity in geopolymer mortar mix designs, where multiple interdependent variables such as fly ash content, SiO₂ and Al₂O₃ percentages, sand content, liquid-to-binder ratio (l/b), curing time, and specimen age influence strength development. A robust dataset of 280 experimentally validated samples was employed, partitioned into training (70%), testing (15%), and validation (15%) subsets. Each model was calibrated using least squares optimization, and evaluated through standard performance metrics such as R², RMSE, and MAE. Among the models, the NLR model achieved the highest predictive performance (R² = 0.9483, RMSE = 5.14 MPa for training; R² = 0.937 for both testing and validation), effectively capturing the nonlinear interdependencies among input variables. MLR and LR demonstrated acceptable but lower predictive accuracies and greater residual dispersion. Residual error plots further substantiated the NLR model’s robustness, with minimal deviation across all datasets. This work contributes novel insights by developing a nonlinear regression framework tailored specifically for geopolymer mortar distinct from more commonly studied concrete systems thereby enhancing the predictive design process for sustainable construction materials. Practically, the developed models offer a valuable framework for performance-based mix optimization of geopolymer mortars, significantly reducing the need for extensive laboratory experimentation. By accurately predicting compressive strength based on mix design and curing parameters, these models facilitate faster and cost-effective decision-making during the material development phase.

We present a novel, chemically-aware framework for predicting the compressive strength of nano-/micro-modified alkali-activated concrete subjected to multi-ionic exposure. A comprehensive dataset of 324 unique mixes—varying binder precursor, nano- and micro-additives, aggregates, silicate–hydroxide ratio, superplasticizer dosage, curing temperature, and ionic exposure—is assembled. We engineer a Chemical Aggressivity Index (CAI) to quantify combined chemical effects and propose a Dual-Path Attention Network (DPAN) that processes material and exposure features in parallel. A hybrid Genetic Algorithm–Particle Swarm Optimisation (GA–PSO) simultaneously tunes network hyperparameters and feature weights, yielding an optimised DPAN with (R^2=0.90), MAE = 2.98 MPa, and RMSE = 4.21 MPa on the test set—surpassing linear regression, SVR-RBF, Random Forest, and XGBoost. Monte Carlo dropout provides reliable uncertainty bands, while SHAP analysis reveals that precursor content, acid concentrations, and CAI most strongly influence strength. The proposed methodology advances data-driven mix design by capturing complex chemical–mechanical interactions and offering actionable insights for resilient, sustainable concrete in aggressive environments.

Over the past few decades, one of the biggest problems facing engineers has been dynamic analysis under the effect of moving forces. These kind of loading is widely used in many different industries, which has made it necessary to evaluate how lively structures respond dynamically to these moving loads. The paper presents a dynamic response of stiffened curved plates under the influence of moving forces at different constant speeds, utilizing the finite element method (FEM) for the investigation. The deflections at diverse locations of the plates can be evaluated by solving the dynamic equations of motion using the Newmark-(beta) method. The dynamic deflection results are compared with FEAST software. A parametric analysis is conducted for various shape, size, loading conditions (moving loads with various constant velocities) and stiffener disposition.

High-rise building construction has increased recently because to factors like population growth, a lack of available residential space, and a lack of adequate land for construction. HRBs are more susceptible to earthquakes as a result of activities brought on by the development in several industries, which has increased seismic activity. The necessity for efficient methods to improve high-rise buildings’ seismic performance has been highlighted by the rising frequency of seismic events. This review presents a comprehensive analysis of recent advancements in the application of TMDs and base isolation systems in high-rise buildings. The paper discusses the fundamental principles, design considerations, and comparative performance of these systems. It also explores the emerging trend of combining TMDs with base isolation to harness the synergistic benefits of both mechanisms. Additionally, the development of more resilient and adaptable high-rise structures in seismically active areas is supported by highlighting current issues, research gaps, and future directions.

The integration of artificial intelligence (AI) into structural engineering has revolutionized design, analysis, and construction processes by automating complex tasks and optimizing decision-making. Among AI-driven tools, ChatGPT has demonstrated significant potential in assisting engineers with structural modeling and analysis. This study introduces SAP2000 API Expert, a custom Generative Pre-Trained Transformer (GPT) based on ChatGPT, for converting narrative structural engineering problems to SAP2000 API Python codes. Unlike conventional methodologies that necessitate users to possess foundational programming or structural engineering competencies, the SAP2000 API Expert provides dual error resolution pathways: a self-debugging approach designed for users with a programming background, or a natural language interface that allows users to describe errors in conversational terms and receive appropriate solutions. Experimental examples, including two benchmarks, were selected to evaluate the GPT’s ability to translate narrative engineering descriptions into executable Python scripts. To validate the accuracy and reliability of the generated scripts, a systematic verification process was conducted by executing the AI-generated codes within SAP2000 and comparing the numerical results with reference solutions from validated technical documentation. The strong agreement between the GPT-generated outputs and benchmark results confirms its computational effectiveness. The innovation is further validated through comparative testing against standard ChatGPT, demonstrating the latter’s inability to generate executable SAP2000 API code, highlighting the significant practical advantages of the domain-specific approach of SAP2000 API Expert. The findings highlight the potential of AI-driven tools in streamlining computational workflows in structural engineering, making design and analysis processes more efficient and accessible. SAP2000 API Expert is accessible for free through this link: https://chatgpt.com/g/g-67b905bf3278819196f4f8b269dfe08c-sap2000-api-ex.

This article centres on the reduction of construction waste through the identification of its sources, accurate waste measurement at project phases, and accurate prediction of waste generation throughout the construction process. Emphasis is placed on the significance of source identification and waste estimation at each project stage to precisely calculate overall waste. The article identifies and categorizes key factors contributing to waste generation, employing the Relative Importance Index (RII) method to determine their significance, severity, and contribution to waste generation. The article delves into the findings to uncover key contributors to trash development across the different phases of construction. These results provide important information for planning waste reduction initiatives. Furthermore, the study delves into the use of an estimating method to quantify the waste generated by key civil engineering materials throughout three distinct phases of a project. Results from this quantification reveal that at the substructure stage sand and bricks, at the superstructure stage bricks, and at the finishing stage external wall finishes experience the highest quantities of waste. Leveraging data from 134 construction sites, the research creates a machine learning model to precisely anticipate waste. The K-NEAREST NEIGHBOR algorithm has an average RMSE of 0.36 and the decision tree method has an average RMSE of 0.41. The model's 88% accuracy supports construction waste management and use. This research uses machine learning and data analysis to quantify and anticipate building waste at various project phases. The study's features and model accuracy enhance construction waste management techniques and provide significant insights for minimising waste throughout the building life cycle.

This study explores the potential of secondary treated wastewater (STW) from three wastewater treatment plants as a viable and sustainable alternative to potable tap water in the production of concrete. In addition to utilizing STW, the concrete mixtures were enhanced with supplementary materials: 10% fly ash, a by-product of coal combustion, and varying dosages (1% to 3%) of sodium nitrite, known for its corrosion-inhibiting properties. The dual aim was to improve the environmental sustainability of concrete while maintaining or enhancing its structural integrity. To evaluate the impact of these modifications, the study conducted a series of standardized performance tests, including assessments of workability using the slump cone method, as well as mechanical property tests for compressive strength, split tensile strength, and flexural strength. The results indicated a 25% reduction in workability for concrete mixed with STW compared to traditional tap water, likely due to variations in the chemical composition of the wastewater. Despite this reduction, the decrease in mechanical strength was relatively minor—compressive strength dropped by only 2.91%, split tensile strength by 4.95%, and flexural strength by 1.75%. These decreases are primarily attributed to the inclusion of fly ash and sodium nitrite rather than the water source itself. To further analyze performance, machine learning models were applied to predict compressive strength. Among them, the Random Forest model demonstrated the highest accuracy, achieving an R² value of 0.87 and a mean squared error (MSE) of 0.86. The findings suggest that STW, in combination with fly ash and sodium nitrite, offers a promising alternative for sustainable concrete production without significantly compromising performance.

Numerous failures occur in steel moment-resistant frame buildings when the steel beam column connection is subjected to earthquake loading. This study includes experimental verification of the connection as well as an investigation of a new steel dog bone fusion connections. This connection's testing and simulation results have been compared to those of another unique fuse connections. After experiencing earthquake damage, this fuse connection can be changed, saving money on building maintenance and restoration. The experimental findings demonstrate a strong agreement with the simulated data. The PEEQ index of connection has been examined. The displacement out of plane behavior has been analyzed. A component-based fuse assembly model has been created, and its initial stiffness values have been compared to experimental and numerical results. The end-plated connection has a higher energy dissipation characteristic, but there is a risk of bolt failure and stress concentration at the beam column face. Based on the extensive analysis, it is possible to conclude that the parabolic fuse assembly is required to provide substantial energy dissipation without causing any damage to the connection's beam column face.