Asian Journal of Civil Engineering最新文献

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.

Amidst the trend towards sustainable construction and the fluctuating availability and cost of steel, bamboo is emerging as a viable alternative for concrete reinforcement due to its ability to enhance tensile strength. This study evaluates the feasibility of using bamboo for concrete reinforcement, with a particular focus on the post-fire flexural behavior and compression properties of bamboo-reinforced concrete (BRC) beams and columns subjected to various fire exposure durations. Bamboo was chemically treated with Sikadur 32 Gel adhesive before being incorporated into the casting of beams and columns. Four groups of treated BRC beams and columns were cast and exposed to 800 °C fire for 0, 30, 60, and 90 min, followed by air cooling. Flexural behavior was analyzed using four-point load tests on beams, while axial compression tests were performed on columns. Load-carrying capacity and failure modes were measured for each specimen. The experimental results show a consistent decline in load-bearing capacity and stiffness with increased fire exposure. Specifically, flexural tests indicate a 51.2% decrease in first crack load and a 53.1% reduction in ultimate load between minimal and prolonged fire exposures. Axial compression tests demonstrated an 88% reduction in ultimate load and a 50% decrease in deflection at ultimate load after 90 min of fire exposure, compared to unheated BRC columns. These findings highlight the importance of material selection and design optimization for enhancing the performance of bamboo-reinforced concrete in fire-prone environments.

Traditional manual inspection approaches for structural health monitoring are time-consuming, unreliable, and sometimes impractical for large-scale structures, motivating the use of automated, data-driven techniques. This study compares different machine learning algorithms and multi-domain features, from simulated data to the health monitoring of an ASCE benchmark building. For that purpose, an ASCE benchmark building is modelled in the ANSYS environment, and time-history acceleration data is collected for healthy and various unhealthy cases. Three distinct features are extracted from the data. (1) statistical features, (2) frequency-domain features (3) time-frequency features, which are utilised as input to the artificial neural networks (ANN), k-nearest neighbours (kNN), and random forests (RF) algorithms. The RF and statistical features combination provides the highest classification accuracy. The findings offer helpful information about selecting the most effective ML algorithms and suitable features for SHM applications.

With an emphasis on high-rise structures exposed to dynamic forces such as seismic and wind forces, this collection of research examines cutting-edge tactics and technology meant to increase the seismic resilience of buildings. Numerous studies look into improving damping systems, such as where to place base isolators (BI) and fluid viscous dampers (FVD). According to these studies, spreading dampers over several levels or the whole building improves seismic stability and lessens undesired structural motions. Another effective method for anticipating seismic reactions and enhancing structural performance is ML (machine learning). Predicting the seismic risk of reinforced concrete moment-resistant frames (RC MRFs), including story displacements and inter story drift, is a key application. For more precise seismic load reconstruction, the application of data-driven dynamic load identification algorithms—like deep learning (LSTM) and artificial neural networks (ANNs)—is also investigated. When taken as a whole, these studies demonstrate how optimization algorithms, machine learning, and sophisticated damping technologies can revolutionize contemporary seismic design and open the door to more durable and affordable tall building options in seismically active areas.

Ensuring the safety and resilience of critical civil and transportation engineering infrastructure requires real-time, intelligent monitoring systems capable of detecting early signs of deterioration. Traditional Structural Health Monitoring (SHM) methods—primarily reliant on manual inspections or threshold-based sensor alerts—struggle to deliver the responsiveness, adaptability, and scalability demanded by modern urban environments in the fields of civil and transportation engineering. This paper introduces a hybrid Artificial Intelligence (AI) framework that integrates machine learning (ML), deep learning (DL), and rule-based reasoning within an edge–cloud architecture for real-time infrastructure monitoring. The system architecture consists of edge-level ML models, including Support Vector Machines and Random Forests, for fast anomaly detection; cloud-level CNN-LSTM networks for temporal pattern recognition; and a rule-based expert system to ensure interpretability and domain consistency across civil and transportation engineering use cases. Data from distributed IoT sensors is pre-processed, normalized, and fused using wavelet transformation, PCA, and statistical extraction methods. Metaheuristic optimization algorithms—Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Grey Wolf Optimizer (GWO)—are employed to fine-tune hyperparameters and select relevant features. Experimental results demonstrate high classification accuracy (up to 96.2%) at the edge, low prediction error (RMSE = 0.085) in cloud-based forecasting, and generalizability under optimization. The proposed hybrid AI system outperforms conventional SHM systems in speed, accuracy, and domain robustness, and is validated for real-world applications in civil and transportation engineering infrastructure.