The main aim of the current paper is to optimize the seismic design of steel moment-resisting frames, with a focus on minimizing life-cycle costs. An efficient and powerful Physics-based metaheuristic algorithm, known as the center of mass optimization, is utilized to search the design space of steel moment-resisting frames' performance-based design optimization problem. Four seismic design optimization scenarios are examined, considering initial cost and seismic total cost as the objective functions to be minimized, with plastic hinge rotation constraints and inter-story drift constraints as the local and global indicators of structural nonlinear behavior, respectively. Within the context of this paper, seismic total cost is formulated as the simple sum of the initial cost and seismic life-cycle cost of the structures. Two design examples, comprising 6-story and 12-story steel frames, are illustrated. The obtained numerical results indicate that optimizing seismic total cost with constraints on both plastic hinge rotation and inter-story drift yields the most cost-effective designs. Additionally, the findings indicate that in optimization processes including both kinds of design constraints, the inter-story drift constraints dominate the optimal designs.
Triple friction pendulum bearings (TFPBs) enhance the seismic resilience of base-isolated buildings. However, optimizing TFPB designs is computationally expensive, necessitating surrogate modeling techniques. This study utilizes structural modeling through OpenSees, while the optimization process, employing the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES), is conducted in MATLAB for the design of TFPB systems. The primary objective is to minimize peak floor acceleration and inter-story drift. A Support Vector Regression (SVR) surrogate simulates the seismic response of models subjected to synthetic earthquake records tailored to site-specific hazards. To closely investigate the SVR method, three kernel functions (Gaussian, Matérn 3/2, and Matérn 5/2) were compared. SVR surrogate modeling reduced computational costs by 77.78%, with prediction differences of less than 2.71% for both objectives, demonstrating high fidelity. Additionally, no single kernel function consistently outperforms others across all optimization problems within the SVR method. In this study, the Gaussian kernel yielded superior results for the peak floor acceleration objective, while the Matérn 5/2 kernel provided better performance for the inter-story drift objective. This indicates that the choice of kernel function may need to be tailored to specific optimization goals.
This research was conducted to explain the drivers and consequences of applying artificial intelligence (AI) approaches through MaxQDA and the best-worst method in the sustainable supply chain of the civil engineering projects. This study aims to be applied research. The participants of both qualitative and theoretical parts of the study were 17 individuals from senior consultants from the construction industry. They have been selected purposefully. Data gathering has been conducted by semi-structured interviews and comparative questionnaires. The reliability of the questionnaire has been assessed by means of non-compliance rates. Qualitative analysis has been performed in order to explain the drivers and consequences of applying AI approaches in the sustainable supply chain of the civil engineering projects. The factors were prioritized through the best-worst method. Findings revealed that the drivers and consequences of applying AI approaches in sustainable supply chains in the civil engineering projects are hardware infrastructures, software infrastructures, non-technical factors (managerial factors), competitiveness, and supply chain sustainability. The first index in ranking was non-technical factors with a weight of 0.344. On the other hand, the second index was sustainability of the supply chain with a weight of 0.267. The third index was software infrastructures, followed by hardware infrastructures in fourth place with weights of 0.229 and 0.122, respectively, and the supply chain competitiveness index was ranked last place with a weight of 0.038.
Earthquakes are among the most devastating natural disasters, and accurately forecasting their magnitude is critical for reducing their impact on life and property. In this study, the effectiveness of advanced machine learning techniques for improving earthquake magnitude prediction is examined using comprehensive seismic data. A hybrid methodology was developed that integrates machine learning models with metaheuristic feature selection methods to enhance accuracy and robustness. Feature selection was performed using Genetic Algorithm, Particle Swarm Optimization, and Simulated Annealing, while ten machine learning models were implemented — ranging from Linear Regression and Decision Trees to Gradient Boosting, XGBoost, LightGBM, and Long Short-Term Memory (LSTM) networks. An ensemble approach was proposed, incorporating Voting and Stacking Regressors to leverage model diversity for improved performance. The Stacking Regressor outperformed all other models with a minimum RMSE of 1.75, MAE of 1.35, and an R-squared value of 0.94. These results demonstrate the potential of ensemble techniques for accurate and reliable earthquake magnitude forecasting, which may contribute significantly to disaster preparedness and risk mitigation.
Over the past 20 years, numerous experimental and numerical studies have been conducted to understand the bond characteristics of externally bonded (EB) FRP-intact (heat-damaged) concrete joints. Consequently, a large database became available to develop models for bond-strength prediction at these joints. An artificial neural network (ANN)-bond model was built, trained and tested using MATLAB®, utilizing more than 500 data points before being statistically analyzed to substantiate its validity for field predictability. The ANN model converged fully at 405 epochs with natural distribution of training and testing data noticed. The use of fourteen hidden layers provided the least prediction error. The performance of the first bond model to consider the impact of elevated temperature was compared to that of well-known literature models The present model demonstrated higher prediction superiority over the different literature models as indicated by the present detailed statistical and sensitivity analyses. For example, using a group of prediction data, the coefficient of determination (R2) and the root mean square error for the present model attained their highest and lowest values at 0.88 and 0.15, respectively, compared to those of the other models tested. The present model captures the trend behavior of bond strength versus the key parameters; namely, compressive strength, maximum aggregate size of concrete, FRP thickness, elastic modulus, as well as FRP bond length and width ratio. The model reflected higher sensitivity to exposing concrete to elevated temperatures than that by the different literature models. The impact of the key parameters indicated becomes marginal when concrete is subjected to temperatures in excess of 400oC. To enable an ease use of the developed model, an empirical equation for bond strength is provided in this work in terms of the different parameters.
