Asian Journal of Civil Engineering最新文献

Machine learning tools have been used in this research to predict the response of a special concentrically braced frame (SCBF) to earthquake using non-linear response history analysis. The target features were the first two modes of vibration (T1 and T2), maximum base shear, and maximum top displacement. A detailed model for three different configurations was modeled in Opens espy to generate the training and testing data. The model captures the nonlinearity of both the material and geometric properties used in the model. A total of 4500 different cases were analyzed in Opens espy (1500 for each configuration). Three machine learning algorithms, Random Forest, XGBoost, and Adaboost, were used in this research; each algorithm was trained to predict the target features mentioned above. Cross-validation technique with 20 folds was used to split the data for training and testing. The input features were different for each target feature to get the highest accuracy of the output. The prediction of the maximum top displacement was performed after the prediction of T1 and T2 because T1 and T2 increase the accuracy of the maximum top displacement prediction. The last prediction is the prediction of the maximum base shear because it depends on the maximum base shear and T1 and T2. A graphical user interface (GUI) was created depending on the trained models.

The building industry has investigated innovation to protect the environment and save resources. The COVID-19 pandemic has limited building supplies, which is raising construction costs. This emphasizes cycle economy-based sustainable growth. C&D trash and other trustworthy resources may be used. C&D wastes dominate solid waste, causing environmental issues. The best method to combat climate change is to cut construction CO₂ emissions. CO₂ emissions are a global issue prompting carbon storage innovation. Alkaline calcium hydroxide and calcium silicate hydrate (C-S-H) in C&D waste may convert CO2 into stable carbonates at ambient temperatures. Temperature, CO₂ partial pressure, time, process route, humidity, and water-to-solids ratio affect C&D CO₂ storage. Due to fast infrastructure development, natural resources are depleting. Industrialization produces CO2, which dominates the atmosphere. CCS involves collecting rubbish, transporting it to a safe place, and burying it to limit CO₂ emissions. Find the source of carbon dioxide, generally a significant point source like a cement mill or biomass power plant, to capture and store it. Corporations should cease emitting tons of CO₂. It may reduce the impact of industrial and residential heating CO₂ on climate change and ocean acidification. Long-term carbon dioxide storage in building materials is novel, although people have poured it into rock formations for decades. The neural network was trained using the same experimental research design, resulting in an ANN model that accurately predicted compressive strength properties (R² ≥ 0.99). This validates the ANN’s effectiveness in response estimation and parameter identification. The ANN technique was also utilized to determine optimal parameters, demonstrating its reliability in predicting and analyzing structural properties.

Fiber-reinforced recycled aggregate concrete (FR-RAC) has recently gained more popularity because of its advantages, high strength, eco-friendliness, and cost-effectiveness. This study uses an advanced machine-learning technique for forecasting the compressive strength of FR-RAC. In this study, an experimental database that contained pertinent data from several previous research was evaluated to train and test using machine learning (ML) techniques and models. To accurately represent the subtle interactions within the dataset, the multivariate analysis identifies and includes essential factors that impact the complicated behavior of FR-RAC in the model. This study presents a hybrid ML model for predicting concrete’s compressive strength by combining several machine learning algorithms in a novel way. To predict the reliability of machine learning models, several algorithms, such as adaptive boosting regressor, support vector regressor, KNN regressor, gradient boosting, and random forest, were developed to help find the interrelated behaviors of parameters. Among all the models used in this study, the Light Gradient-Boosting Machine (GBM) outperforms (R² = 0.90) other models, each of which was fitted to a different portion of the training dataset. Additionally, the SHAP analysis revealed that recycled coarse aggregate has an inverse impact on the strength of FR-RAC. Overall, the outcomes of this study can significantly contribute to cost and material reduction by predicting the compressive strength of FR-RAC without the need for extensive laboratory testing and promoting more efficient use of resources.

This study investigates the application of the Fruit Fly Optimization Algorithm (FOA) in enhancing the predictive performance of the Light Gradient Boosting Machine (LightGBM) model for smart sustainable architecture. Key features, including Energy Consumption, Water Usage, Material Cost, CO2 Emissions, and Design Flexibility, were selected using FOA to optimize the model’s predictive accuracy. The FOA-based feature selection significantly improved across all performance metrics: Accuracy increased from 0.85 to 0.88, Precision from 0.80 to 0.84, Recall from 0.78 to 0.82, and the F1-Score from 0.79 to 0.83. Moreover, the Root Mean Square Error (RMSE) decreased from 0.25 to 0.22, while the Area Under the Curve (AUC) improved from 0.76 to 0.8625. These findings underscore the effectiveness of FOA in refining feature selection, thereby enhancing the efficiency and reliability of predictive models in sustainable architectural design. The study highlights the potential of advanced optimization algorithms in developing more adaptive, resource-efficient, and sustainable architectural solutions.

The present work aimed to study the artificial neural network (ANN) and its effectiveness for prediction of compressive strength (f_c). Genetic algorithm (GA) was used for optimization of five different types of ANN networks viz. multilayer Perception network (MLP), generalized feedforward network (GFF), principal component analysis network (PCA), time lagged recurrent networks (TLRN), recurrent networks (RN). A 272 data set of f_c was obtained from the various literatures and used for training, testing and validation. Mean square error (MSE), mean absolute error (MAE) and correlation coefficient (R) used as validation criteria. Water to cement (w/c) ratio, maximum size of aggregate, curing days and cement content etc. were used as input parameter for prediction of f_c. The result reveals that MLP has more precise compared with GFF, PCA, TLRN, RN, the observed values of R is 0.97, MSE is 42.30 and MAE is 5.57, which indicates the model is best fir for prediction of f_c.

The construction industry faces the critical challenge of balancing project time, cost, and carbon emissions to achieve sustainable development. This study introduces a Time–Cost–Carbon Emission Trade-Off (TCCET) model, optimized using the Non-dominated Sorting Genetic Algorithm III (NSGA-III), to address these conflicting objectives. The TCCET model evaluates various execution modes for construction activities, such as groundwork, excavation, footing, formwork, and finishing, taking into account their respective impacts on time, budget, and carbon emissions. By applying NSGA-III, the model generates a set of Pareto-optimal solutions, offering decision-makers diverse trade-offs among these objectives. A practical case study demonstrates the model’s effectiveness in real-world scenarios, yielding flexible and efficient solutions that support informed decision-making in construction management. Comparative analysis with existing optimization models and sensitivity analysis highlight the superior performance of NSGA-III in addressing time, cost, and environmental impact simultaneously. This study’s findings emphasize the potential of NSGA-III to guide sustainable construction practices, significantly reducing environmental footprints without compromising project timelines or costs. The developed framework aligns with global sustainable development goals, providing valuable insights for the construction industry’s transition to sustainable practices.

Improving ventilation systems is essential for better indoor air quality, energy efficiency, and overall building performance. This study introduces a new optimization model to tackle the trade-offs between time, cost, and indoor air quality (IAQ) in ventilation system retrofitting projects. Using the Non-dominated Sorting Genetic Algorithm III (NSGA-III), the model evaluates various retrofitting options, including upgrades for ventilation capacity, energy efficiency, air quality, noise reduction, and aesthetic improvements. Each option is assessed for its impact on project duration, cost, and indoor air quality. The goal is to find the best combinations of these options that minimize both project time and cost while improving indoor air quality and meeting resource constraints. The NSGA-III algorithm generates a set of optimal solutions, providing a range of choices for balancing these factors. A comparison with existing methods shows that this new approach offers better solutions for managing these trade-offs. By selecting the most effective solution from these options using a weighted sum method, the study demonstrates NSGA-III’s power in handling complex optimization problems. This model supports better decision-making in retrofitting projects, advancing both sustainability and indoor environment quality.

The construction industry plays a pivotal role in global carbon emissions, prompting a critical need for sustainable infrastructure-development practices. Retaining walls, which are essential for stabilising earth and water pressure in civil engineering projects, represent a significant opportunity to mitigate environmental impacts through material optimisation. This study investigated the design efficiency and embodied carbon and cost implications of cantilever retaining walls constructed with concrete and steel sheet piles. This study employs a thorough methodology that incorporates quantitative studies of the cost and embodied carbon at varying retaining wall heights. The environmental effects and financial viability of the concrete and steel sheet piles were assessed using standardised procedures and local market data. The results indicate that in every height category, concrete sheet piles show consistently reduced total costs and embodied carbon when compared to their steel equivalents. Superior environmental sustainability is demonstrated by concrete, where the embodied carbon levels gradually increase as the wall height increases. On the other hand, steel provides better load-bearing capability, but at a higher cost to the environment and economy, which is especially noticeable in taller structures. This study offers significant perspectives for engineers and other relevant parties to enhance design results that harmonise ecological responsibility with cost-effectiveness in building methods.

The health monitoring of concrete structures is of principal concern to avoid major accidents. Presently, many large-scale structures have been constructed throughout the world and in India. Therefore, there is an urgent need for sensor-aided research to keep all these large infrastructural facilities for the long life in an uninterrupted manner. As per the available literature, the Acoustic Emission (AE) sensor data and its deployment for the development of an artificial intelligence (AI) model is most suitable for health monitoring of these types of structures. Researchers have used the signal processing method. However, the AI models have significantly reduced the effort as well as errors in the computation process. In this study, an experimental investigation is done using the AE system for data generation. A good number of concrete slabs of different grades were cast and used for generating data deploying the Pencil Lead Break (PLB) approach. The generated data was utilized for finding the damage location using the WT method and AI models. The developed AI model is more effective in the health monitoring of concrete structures as the error in calculation is less as compared to the WT method. The model is also validated by identifying the damage source (simulated) in the concrete slab. This approach can be utilized for real-time health monitoring of large-scale concrete structures comprised of slab-like components without any interruption. Results show promising trends for further research for making the health monitoring process in wider application of civil engineering structures.