This paper presents an advanced vibration analysis of Al2O3 nanocomposite-reinforced concrete bridge structures resting on an innovative elastic foundation using the Carrera Unified Formulation (CUF). The primary objective is to investigate the dynamic response of these bridges under various loading conditions, accounting for the reinforcing effects of Al2O3 nanocomposites within the concrete matrix. The formulation incorporates a novel elastic foundation model designed to more accurately simulate realistic boundary conditions and soil-structure interaction. The accuracy and reliability of the CUF-based vibration analysis are further validated using nondestructive testing (NDT) techniques, which enable the detection of potential damage and anomalies in the bridge structures. Moreover, a machine learning (ML) algorithm is employed to predict the vibrational characteristics, facilitating an efficient comparison with the results obtained from CUF and NDT. The combination of theoretical modeling, experimental verification, and ML predictions highlights the robustness of the proposed method. The results demonstrate the effectiveness of using Al2O3 nanocomposites to enhance the mechanical properties of bridge structures, improving their vibrational performance, stability, and longevity. This study provides a comprehensive framework for future applications in bridge engineering, combining high-fidelity numerical methods with state-of-the-art testing and computational techniques.

Simulation-optimization methods are widely used in dewatering optimization. However, traditional simulation-optimization methods do not address the optimization of well screen length and depth. This study proposes a modified simulation-optimization method for confined aquifer dewatering optimization, which is capable of determining the optimal screen length and depth. The proposed method is based on the linear programming method, and the multivariate adaptive regression splines method is also introduced to develop the prediction model for the parameters required in the linear programming model. A hypothetical case of deep excavation dewatering was optimized using the proposed method to demonstrate its feasibility, with the optimal pumping rate, screen length and depth of each well computed. Furthermore, parametric studies were performed to investigate the effects of some key factors on the optimization results, such as the number of considered pumping wells, required drawdown, insertion ratio of the waterproof curtain, aquifer anisotropy coefficient, and prescribed well screen. The results show that optimal total pumping rate and screen length generally increase with increasing required drawdown and aquifer anisotropy coefficient, while they decrease with increasing well number and insertion ratio of the waterproof curtain. Adjusting screen length is more critical to the optimization results since lower screen depth is always preferred. Optimizing well screen is more essential for higher well number, insertion ratio of the waterproof curtain, and lower aquifer anisotropy coefficient.