Asian Journal of Civil Engineering最新文献

Underground metro stations play a crucial role in urban transportation systems, which necessitating the need for effective structural design and maintenance. The use of lightweight materials such as backfill above underground metro station roofs has gained significant attention due to their potential in reducing internal forces on the structure. This study aims to investigate the effect of using geofoam as a backfilling material on reducing the internal forces within underground metro stations elements. EPS Geofoam, a lightweight and cellular plastic material, offers various advantages, such as low density, high compressive strength, and excellent insulating properties. These properties make it a prominent candidate for mitigating the internal forces induced by the applied loads on underground metro station roofs. By replacing traditional backfilling materials with geofoam, the overall weight of the fill above the roof is significantly reduced, leading to a reduction in the applied loads and subsequently minimizing the internal forces experienced by the structure. To assess the effect of geofoam, a comprehensive numerical analysis was conducted by using finite element modeling through PLAXIS2D package software. Various scenarios of loading and stag of construction were considered, simulating different types of live loads. The study encompassed a comparison of internal forces, encompassing bending moments, shear forces, and axial forces, between the conventional backfill and the backfill utilizing EPS geofoam. The primary focus of this research is to emphasize the advantages associated with integrating geofoam as a material for backfilling. In addition to the potential of geofoam in reducing internal forces and optimizing the structural behavior of underground metro stations. The implementation of geofoam-based backfilling can lead to enhance the safety, increase cost-effectiveness, and improve sustainability of underground metro station structures. The results of the study demonstrated that the incorporation of geofoam as a backfilling material above the roof of underground metro stations leads to a substantial reduction in internal forces. The lightweight nature of geofoam significantly decreases the bending moments, shear forces, and axial forces acting on the roof structure, improving its overall performance and extending its service life.

Cement serves as a crucial binder in concrete production. Cement consumption is projected to reach around 4.4 billion tons in 2020, up from approximately 1.6 billion tons in 2000. By 2050, it is expected to increase by 13 to 23%. The environmental impact of cement production is significant, as producing one ton of cement emits roughly 0.73 to 0.99 tons of carbon dioxide. The cement industry is responsible for about 7–8% of global CO₂ emissions and accounts for 26% of the world’s total CO₂ emissions. This study explores the feasibility of using seashell ash powder (composed mainly of calcium carbonate) as a partial cement replacement in concrete production. This study highlights the potential of seashell ash powder as a sustainable supplementary cementitious material, improving concrete workability and strength properties (Compression, flexural and split tensile) while promoting environmental sustainability through waste utilization. This study analyses the gap using Response Surface Methodology to optimize seashell ash powder ranging from 2 to 10% with different water-cement ratios ranging from 0.4 to 0.6. Results showed that higher seashell ash powder levels, combined with lower water-cement ratios, significantly enhanced compressive strength and workability. Optimal mix designs were identified, with the best composition featuring 10.94% seashell ash powder and a 0.52 water-cement ratio, achieving a desirability score of 68.81%.

In today’s transportation networks, bridges play an essential role as conduits that allow efficient access to a variety of locations. These structures are still vulnerable to outside pressures, though, and doing so can result in serious harm, especially during seismic occurrences. In this research, we model and analyze reinforced concrete (RC) T-beam bridges with elastomeric bridge bearings in order to thoroughly assess the seismic behavior of bridge components. We build and examine several span bridge models with CSI Bridge Software, altering pier heights and bearing stiffnesses in a methodical manner. In this work, we evaluate an RC bridge’s seismic susceptibility by taking regionally variable ground motions into account. Fragility curves, which are crucial instruments for evaluating risk, are at the center of our research. The probability of failure is represented by these curves over the whole load spectrum. Typically, fragility curves plot estimated probabilities (such as deflection) against ground motion parameters, providing insights into the likelihood of exceeding specific deformation limits during seismic events. Our research aims to create accurate fragility curves, facilitating precise loss calculations for bridge structures. By employing artificial neural networks (ANNs) and long short-term memory (LSTM), this research addresses uncertainties associated with influencing factors. It has been discovered that the inputs and outputs of the ANN and LSTM models are, respectively, the influencing traits and fragility parameters of significant components.

This study investigates the effects of Ground Granulated Blast Furnace Slag (GGBS) on the thermal stability and compressive strength of concrete, aiming to identify novel insights and contribute to sustainable construction practices. The experimental approach integrates innovative methodologies to analyse concrete properties and assess the suitability of GGBS as a supplementary cementitious material. Through meticulous sample preparation and testing, a nuanced relationship between GGBS content and concrete performance is observed. Key findings reveal that moderate levels of GGBS replacement enhance compressive strength, supporting previous research. However, beyond a certain threshold, diminishing returns are observed, highlighting the importance of optimizing GGBS content in concrete mix designs. Microstructural analysis unveils reductions in porosity and alterations in hydration products with increasing GGBS content, indicative of improved mechanical properties and thermal stability. The results underscore the potential of GGBS as a sustainable alternative in concrete production, offering both environmental benefits and performance enhancements. By leveraging GGBS, engineers can achieve a balance between structural integrity, thermal resilience, and environmental sustainability in concrete structures.

This paper introduces a time-cost trade-off optimization model developed for Indian highway construction projects, leveraging the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) methodology to minimize both project duration and cost simultaneously. The model incorporates critical constraints such as precedence relationships and resource availability, ensuring practical applicability in complex construction environments. Through a detailed case study, the study demonstrates the model’s efficacy in aiding decision-making and analyzing trade-offs inherent in highway projects. By offering insights into scheduling decisions, stakeholders can enhance project efficiency and cost-effectiveness, addressing the intricate challenges of infrastructure development in India. The NSGA-II algorithm excels by efficiently identifying Pareto optimal solutions that balance project duration and cost effectively, surpassing traditional trade-off models. This systematic approach supports India’s economic growth objectives by optimizing infrastructure development processes. Moreover, the weighted sum technique is employed to select one solution from obtained Pareto-optimal solution. Also, the study underscores the algorithm’s robust performance in managing complex construction projects, contributing to improved project management practices within the Indian context.

This work intends to demonstrate the importance of deployment of structural health monitoring (SHM) systems for monitoring real-time detection of damages or defects in structural components and forecast the outstanding life of bridge structures. The main objective is to focus on designing an optimized sensor network and implementation to SHM of bridges. Adopting an optimum number and appropriate location of sensors is of utmost importance for integration of SHM systems that influence the accuracy of assessment, system performance and the total cost. A computational framework is developed in Python which provides optimal configurations for sensors and actuators to be deployed with ultrasonic guided-waves for non-destructive testing of bridges in service. An objective function is developed for convex entropy with the goal of decreasing uncertainty while optimising the monitoring system’s expected accuracy in locating structural degradation. The framework is designed to deal with two types of materials: isotropic and anisotropic composite materials. Two plates made of composite material and aluminium metal are used to show the usefulness and efficiency of the current framework. The best actuator and sensor configurations for the ultrasonic guided wave based use in bridges have been discovered. It is observed that for the composite plate, the current function value is 94.71597173 and for the metal plate it is 55.6033447. Finally, in the future the authors will validate the results obtained from the present framework with that of the experimental work using the equipment to be delivered by BeanAir-Germany.

The fundamental period plays an important role when a structure is designed for seismic load. Infill walls are non-load-bearing walls created mostly from masonry, concrete, and other heavy materials, filled in the primary structural frame for a proper structural cladding system. As a result, this infill wall will increase the stiffness of the structure, thereby fundamental time period is significantly changed. Most of the studies on the fundamental period do not give much importance to the infill walls even though it is crucial to be analyzed. In this work, we propose an automated and efficient analysis method for predicting the fundamental period of infill Reinforced Concrete frames using machine learning techniques. As the nature of dependency of different independent variables considered in this study is unknown, different regression techniques were chosen for this purpose. So, we rely upon an exceptional machine learning technique called ensemble learning, which combines predictions from different models to deduce the final prediction more accurately. The storey numbers, the number of spans, length of span, stiffness of infill wall, and percentage of openings are set as input factors, while the value of the fundamental time period is chosen as an output. The proposed regression model's correctness is verified by comparing it to existing formulae in the literature. As a result, in comparison to statistical models, the linear regression model shows an r2 value of 0.98921 and has better ability, flexibility, and accuracy.

The urgent necessity to strengthen structures with substandard designs has been demonstrated by recent earthquakes. Natural fiber reinforced polymers (NFRPs) provide an affordable, sustainable means of reinforcement, yet accurately forecasting their performance is still a difficult task. The application of soft computing approaches to forecast the compressive strength (CS) of concrete specimens reinforced through various NFRPs is examined in this work. In the present study, three approaches were utilised: AdaBoost, Random Forest (RF), and XGBoost. To evaluate the performance of each soft computing technique, several statistical indicators were calculated, including the Coefficient of Determination (R²), Nash–Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), Wilmott Index (WI), Mean Absolute Error (MAE) and Mean Squared Error (MSE). The results demonstrated that the XGBoost model outperformed the other models, with an R² of 0.85, RMSE of 5.05, MAE of 3.83, MSE of 25.48, WI of 0.96, and NSE of 0.85 during the testing stage. SHAP analysis revealed that the unconfined CS of the concrete specimen (fc) had the greatest impact on Forecasting the CS of NFRP. These findings suggest that soft computing has considerable potential to forecast the CS of concrete reinforced utilising NFRPs. XGBoost is a model that generates the most precise forecasts out of all the others, making it an essential tool for engineers who aim to improve the performance and design of structures constructed of sustainable materials.

This study explores the possibility of reusing quarry waste in the form of powdered sand to produce environmentally friendly sand concrete, with a focus on addressing environmental sustainability. The investigation comprised the preparation of five concrete mixtures with differing limestone sand ratios: 0%, 40%, 50%, 60%, and 70%. To evaluate the impact of limestone sand incorporation, analysed physical and mechanical characteristics through tests such as density, compressive and flexural strength, ultrasonic pulse velocity, dynamic elastic modulus, and microstructure analysis. Findings indicate substantial enhancements in sand concrete properties due to the integration of limestone sand, with the 60% ratio emerging as the most productive. The study underscores limestone sand’s capability to not only improve sand concrete quality but also offer a sustainable method for quarry waste recycling. It demonstrates the beneficial impact of limestone sand used in sand concrete and advocates for its application as a sustainable quarry waste recycling strategy across the construction industry’s various sectors.

Decades of research have focused on improving the ability of structures to withstand dynamic loads. Numerous studies have established the effectiveness of Carbon Fiber Reinforced Polymers (CFRP) sheets in strengthening of existing RC walls. This research examines the effectiveness of using CFRP sheets to strengthen Nickel-Titanium (NiTi) alloy walls. Two large-scale NiTi-walls with an aspect ratio of 1.9 were repaired and strengthened by using unidirectional Sika Wrap 230-C CFRP sheets, then tested experimentally under lateral cyclic loading. Moreover, this study investigates the effectiveness of a strengthening technique that uses variable configurations of CFRP in RC-NiTi walls to improve their structural performance in terms of lateral load capacity, ductility, inter-storey drift, and hysteretic behavior. Furthermore, machine learning models (Fitting Neural Networks (FNN)) are developed to analyse the impact of various factors on shear wall strengthening techniques, including cross-sectional area, concrete strength, and CFRP sheet intensity. The proposed models’ validation demonstrates a high degree of agreement with the experimental results. The study demonstrated a remarkable improvement in shear walls strengthened with CFRP sheets, resulting in an average 38% increase in lateral load capacity and a 15% enhancement in energy dissipation.