Asian Journal of Civil Engineering最新文献

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.

Over the last decade, the use of fiber-reinforced polymer (FRP) composites for enhancing the performance of reinforced concrete (RC) structures has gained popularity due to its outstanding mechanical performance. In this study, novel advancements are achieved through the development of a three-dimensional ABAQUS model that explicitly captures the interactions between critical parameters, specifically CFRP length and thickness. For this, the finite element model was validated through two experimental studies on RC beams from the literature. Each beam featured a rectangular cross-section and was subjected to a four-point loading test, with variations in the length and strip configuration of the carbon fiber-reinforced polymer (CFRP) plate. A perfect bond model was applied at the concrete-CFRP interface, while the concrete behavior was simulated using the concrete damage plasticity (CDP) model. The analysis results showed a good correlation with experimental studies. The parametric study revealed that optimizing CFRP length and thickness significantly improves load capacity, with diminishing returns beyond certain thresholds. Longer CFRP laminates significantly enhance both load-carrying capacity and total energy absorption. The ultimate load enhancement follows a near-linear relationship with the bonded area. Key results show longer CFRP laminates substantially increase load capacity and energy absorption, while a CFRP thickness of 1.2 mm optimizes strength, ductility, and energy absorption. Beyond this thickness or optimal length threshold, gains diminish significantly and ductility reduces. These findings offer insights into CFRP strengthening strategies and highlight the FEM model’s effectiveness in predicting structural behavior.

This study uses the curvature damage index method based on artificial neural networks to investigate structural faults. The damages are inspected at multiple locations by using a pinned–pinned supported beam and a tubular propped cantilever beam with a rectangular cross-section. Initially, the experimental and numerical data were utilized to observe the mode shapes of undamaged and damaged beam models. The mode shape data was utilized to investigate the curvature damage index for various damage severities. An artificial neural network (ANN) was utilized for training the experimental data to eradicate undesirable peaks caused by data errors in displacement mode shape data. By using the absolute curvature damage index, the numerically obtained modal parameters (displacement mode shape) are highly suitable for calculating damage areas without ANN training. Further, the mode shape curvature was developed by using central difference approximation for each damage case after obtaining the frequency response (FR) data. To display the damages in beam specimens, a modified curvature damage index (MCDI) is created by using trained data. The study has demonstrated that the proposed technique, which utilises ANN-trained FR data instead of directly using untrained FR data, is capable of identifying structural damages with greater accuracy.

Elevated reinforced concrete (RC) water tanks are critical lifeline structures whose seismic performance is governed by fluid–structure interaction (FSI) and staging systems. Conventional fragility curves developed through incremental dynamic analysis (IDA) provide probabilistic insights but demand extensive nonlinear time‐history analyses, limiting their practical use. This study introduces a hybrid IDA–machine learning (ML) framework that couples IDA with support vector regression (SVR) and a physics-informed neural network (PINN) surrogate to accelerate fragility curve generation for three elevated water tanks (75 m³, 320 m³, 1008 m³). Finite element (FE) models in SAP2000 embed Housner’s added mass to capture hydrodynamic effects. IDA under 22 far-field ground motions produces 738 nonlinear response samples of peak inter-story drift ratio (IDR) across spectral acceleration (Sa), peak ground velocity (PGV), and geometric inputs. SVR and PINN models are trained on this dataset, with Bayesian hyperparameter tuning and Shapley additive explanations (SHAP) interpretability. PINN outperforms SVR (R2 = 0.99 vs 0.95; RMSE = 0.0008 vs 0.0021), sustaining errors below 5% at collapse prevention (CP) thresholds while delivering millisecond-scale inference. ML-derived fragility curves align with IDA baselines for immediate occupancy (IO), life safety (LS), and CP states within 0.05 g medians. Global sensitivity and input uncertainty analysis via Saltelli quasi-Monte Carlo highlight standard deviation (SD) as the principal driver of IDR variance (> 55%) and define a 5%–95% IDR band of 0.005–0.045. The proposed approach cuts computational time by orders of magnitude while preserving probabilistic rigor, enabling rapid, code-compliant seismic risk assessment of elevated RC tanks.

Many different technical, meteorological, environmental, demographic, economic, and legislative issues are taken into consideration while developing and implementing systems for solid waste management. Understanding such complex nonlinear systems is challenging. In the city of Chennai at Tamil Nadu, where the solid waste management is a major concern for people’s health and where many new technologies are being implemented, environmental management is still inadequate. The purpose of this research is to apply the waste recycle management (WARM) to analyze potential futures and choose the most viable strategies for the long-term management of MSW in the Chennai metropolitan area (Tamil Nadu, South India). The life cycle assessment studies for Chennai City’s municipal solid waste management system show that landfilling and transportation emissions harm the environment. The sensitivity analysis to look how changing recycling rates influenced our ability to employ landfilling, composting, anaerobic digestion and etc.,. Based on the sensitivity analysis findings, impact categories and waste recycling are incompatible with one another. Additionally, the air quality in Chennai has drastically deteriorated due to all of the garbage dumps. Understanding how air quality is managed in various urban zones is vital, since this may have far-reaching consequences.

Eco-friendly concrete is a sustainable construction material designed to reduce environmental impact by incorporating recycled materials and minimizing carbon emissions. However, traditional empirical methods often fail to accurately predict its performance due to the complex interactions among novel additives such as glass fiber and marble dust. This study presents an integrated experimental and machine learning framework to predict and optimise concrete’s compressive, flexural, and split tensile strengths over 7, 14, 28, and 56-day curing periods. Advanced models, Artificial Neural Networks (ANN), Support Vector Regression (SVR), Random Forests (RF), Extreme Gradient Boosting (XGBoost) and hybrid CNN-LSTM (Convolution Neural Networks and Long Short Term Memory) were evaluated. Among these, the hybrid CNN-LST demonstrated superior performance, achieving R² values of 0.999, 0.999, and 0.999 for compressive, flexural, and split tensile strengths, respectively, with a minimum RMSE of 0.0095 for compressive strength prediction. Feature importance analysis revealed curing time as the most influential variable, while the sensitivity analysis suggested optimal strength to be maximum at approximately 8–10 kg of marble dust and 15–21 kg of glass fiber. A multi-objective Genetic Algorithm (GA) and NSGA—II (Non -dominated sorting algorithm) were used to optimize the mix design, yielding predicted 56-day strengths of 37.24 MPa (compressive), 4.27 MPa (flexural), and 3.42 MPa (split tensile). Monte Carlo simulations were used to assess the uncertainty and enhance robustness. The proposed framework significantly reduces the experimental workload while offering a cost-effective, scalable strategy for developing sustainable high-performance concrete using industrial waste.