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Full-Text Articles in Structural Engineering
Machine Learning-Based Seismic Damage Assessment Of Residential Buildings Considering Multiple Earthquake And Structure Uncertainties, Xinzhe Yuan, Liujun Li, Haibin Zhang, Yanping Zhu, Genda Chen, Cihan H. Dagli
Machine Learning-Based Seismic Damage Assessment Of Residential Buildings Considering Multiple Earthquake And Structure Uncertainties, Xinzhe Yuan, Liujun Li, Haibin Zhang, Yanping Zhu, Genda Chen, Cihan H. Dagli
Civil, Architectural and Environmental Engineering Faculty Research & Creative Works
Wood-frame structures are used in almost 90% of residential buildings in the United States. It is thus imperative to rapidly and accurately assess the damage of wood-frame structures in the wake of an earthquake event. This study aims to develop a machine-learning-based seismic classifier for a portfolio of 6,113 wood-frame structures near the New Madrid Seismic Zone (NMSZ) in which synthesized ground motions are adopted to characterize potential earthquakes. This seismic classifier, based on a multilayer perceptron (MLP), is compared with existing fragility curves developed for the same wood-frame buildings near the NMSZ. This comparative study indicates that the MLP …
Deep Reinforcement Learning For Approximate Policy Iteration: Convergence Analysis And A Post-Earthquake Disaster Response Case Study, Abhijit Gosavi, L. (Lesley) H. Sneed, L. A. Spearing
Deep Reinforcement Learning For Approximate Policy Iteration: Convergence Analysis And A Post-Earthquake Disaster Response Case Study, Abhijit Gosavi, L. (Lesley) H. Sneed, L. A. Spearing
Engineering Management and Systems Engineering Faculty Research & Creative Works
Approximate Policy Iteration (API) is a Class of Reinforcement Learning (RL) Algorithms that Seek to Solve the Long-Run Discounted Reward Markov Decision Process (MDP), Via the Policy Iteration Paradigm, Without Learning the Transition Model in the Underlying Bellman Equation. Unfortunately, These Algorithms Suffer from a Defect Known as Chattering in Which the Solution (Policy) Delivered in Each Iteration of the Algorithm Oscillates between Improved and Worsened Policies, Leading to Sub-Optimal Behavior. Two Causes for This that Have Been Traced to the Crucial Policy Improvement Step Are: (I) the Inaccuracies in the Policy Improvement Function and (Ii) the Exploration/exploitation Tradeoff Integral …
The Application Of Fuzzy Analytic Hierarchy Process In Sustainable Project Selection, Rakan Alyamani, Suzanna Long
The Application Of Fuzzy Analytic Hierarchy Process In Sustainable Project Selection, Rakan Alyamani, Suzanna Long
Engineering Management and Systems Engineering Faculty Research & Creative Works
The project selection process is a crucial step in sustainable development. Effective sustainable development depends on the ability to select the appropriate sustainable project to implement to ensure that the desired goals are met. Some of the most common characteristics or criteria used in evaluating sustainable projects include novelty, uncertainty, skill and experience, technology information transfer, and project cost. Prioritizing these criteria based on relative importance helps project managers and decision makers identify elements that require additional attention, better allocate resources, as well as improve the selection process when evaluating different sustainable project alternatives. The aim of this research is …