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Calculating impact forces in asymmetrical lateral structures has been a complex challenge that spans decades in engineering. Traditional models often fall short due to the inherent complexity of asymmetrical members and the need for significant computational resources or a vast pool of training data. This paper develops an approximate formula for accurately calculating the impact force of asymmetrical lateral-impact members under lateral impact. Existing methods for assessing impact forces have been limited in their application due to the inherent complexity of asymmetrical members and the significant computational resources or extensive training data they often require. Our approach employs the residuals margin method, and Laplace transforms to derive an efficient and accurate formula for impact force calculation. The paper rigorously validates this Formula through experimental testing, demonstrating high precision with an error margin of less than 5%. Further validation against diverse impact data from multiple studies on different materials and loadings under static and dynamic conditions confirmed the Formula's consistency. Despite simplifying assumptions, this research contributes a novel and computationally efficient approach for calculating impact forces. The formula offers engineers a practical tool while advancing a fundamental understanding of asymmetric impact dynamics. Rigid experimentation verified its significant accuracy, establishing the formula as a valuable structural impact analysis and design resource. This research presents an analytical formula for calculating impact forces on structures like buildings, bridges, and vehicles experiencing asymmetrical lateral impacts. Such impacts commonly occur due to falling debris, vehicle collisions, seismic pounding, and derailed train strikes. However, existing design formulas often oversimplify impact mechanics or require complex simulations. The proposed method provides engineers with a simple spreadsheet-compatible equation relating impact force directly to tangible mechanical quantities like momentum. This enables rapid impact load assessments essential for performance-based design against accidental hazards. The formula was validated through laboratory impact tests on reinforced concrete slabs, demonstrating precision within 5% of measured forces. Additional validations against published experimental and simulation data on different construction materials confirmed accuracy. The proposed formula equips structural engineers and safety analysts with a practical impact analysis tool by offering a computationally efficient approach with proven reliability. It facilitates assessing design performance for asymmetrical impact load scenarios, helping improve resilience for critical facilities subjected to hazardous lateral impacts. [ABSTRACT FROM AUTHOR]

The interface bond strength between carbon fiber-reinforced polymer (CFRP) layers and concrete is a crucial metric for determining the mechanical properties of CFRP-reinforced concrete. This bond strength is essential for evaluating CFRP-reinforced concrete's performance and ensuring the materials' structural integrity. A database was established using the experimental data in the literature to evaluate the interface bond strength. This database comprised 360 groups of different conditions test results of CFRP-reinforced concrete, which were used to create a prediction model using an artificial neural network. The database was randomly divided into two data sets: 310 groups were used for training the neural network model and 50 for simulated prediction. A three-layer artificial neural network model was trained using the backpropagation algorithm, which is widely used in artificial neural networks. The model's input layer considered seven parameters, including the type of CFRP layer, surface form, CFRP layer thickness, anchorage length, failure mode, concrete compressive strength, and normalized concrete cover thickness. These parameters were selected based on their known influence on the interface bond strength between the CFRP layers and concrete. The output layer of the model represented the interface bond strength between the CFRP layers and concrete. The model's results indicated that the backpropagation (BP) neural network model had strong capability of prediction and generalization. The predicting error was minimal, a crucial aspect of the model's accuracy. Further, this approach allows for integrating many factors that influence the interface bond strength between the CFRP layers and concrete, providing accurate predictions of the bond strength. It can be used as a valuable tool for evaluating the performance of CFRP-reinforced concrete. This research develops an accurate method to predict the bond strength between CFRP layers and concrete using artificial neural networks. A strong bond is crucial for the structural integrity of concrete reinforced with CFRP. The neural network model considers factors like the type and thickness of CFRP used, how the concrete surface is prepared, and the concrete's strength. Engineers can use this neural network tool to evaluate how well CFRP will reinforce specific concrete mixtures and structures before construction. This allows structures to be designed and built with optimal, cost-effective use of CFRP to reinforce concrete in applications like bridges and buildings. The neural network approach integrates many technological and material factors into one predictive model, providing a useful evaluation method for the construction industry. [ABSTRACT FROM AUTHOR]