Abstract In this study, three machine learning techniques, the XGBoost (Extreme Gradient Boosting), LSTM (Long Short-Term Memory Networks), and ARIMA (Autoregressive Integrated Moving Average Model), are utilized to deal with time series prediction tasks for coastal bridge engineering. The performance of these techniques is comparatively demonstrated in typical cases, wave-load-on-deck under re...

While software design is increasingly based on models, requirements engineering is generally performed using structured text; as a consequence, only a weakly structured connection to design is possible. Model-based requirements engineering can bridge this gap. With AutoRAID we introduce a tool prototype supporting a structured and integrated requirements model, and operations for refinement and...

Some features of bridge aerodynamics of wind engineering are reviewed at this time, in view of the collapse of the Tacoma Narrows Bridge and also to celebrate Professor Alan G. Davenport’s 40 years of contribution to the field. Primary highlighted topics are: description of motion-dependent forces for flutter instability, presentation of gust responses caused by turbulent winds and suppression ...

Summer bridge programs are designed to help transition students into the college learning environment. Increasingly, bridge programs are being developed in science, technology, engineering, and mathematics (STEM) disciplines because of the rigorous content and lower student persistence in college STEM compared with other disciplines. However, to our knowledge, a comprehensive review of STEM sum...