Bayesian model updating has received considerable attention and has been extensively used in structural damage detection. It provides a rigorous statistical framework for realizing structural system identification and characterizing uncertainties associated with modeling and measurements. The Markov Chain Monte Carlo (MCMC) is a promising tool for inferring the posterior distribution of model parameters to avoid the intractable evaluation of multi-dimensional integration. However, the efficacy of most MCMC techniques suffers from the curse of parameter dimension, which restricts the application of Bayesian model updating to the damage detection of large-scale systems. In addition, there are several MCMC techniques that require users to properly choose application-specific models, based on the understanding of algorithm mechanisms and limitations. As seen in the literature, there is a lack of comprehensive work that investigates the performances of various MCMC algorithms in their application of structural damage detection. In this study, the Differential Evolutionary Adaptive Metropolis (DREAM), a multi-chain MCMC, is explored and adapted to Bayesian model updating. This paper illustrates how DREAM is used for model updating with many uncertainty parameters (i.e., 40 parameters). Furthermore, the study provides a tutorial to users who may be less experienced with Bayesian model updating and MCMC. Two advanced single-chain MCMC algorithms, namely, the Delayed Rejection Adaptive Metropolis (DRAM) and Transitional Markov Chain Monte Carlo (TMCMC), and DREAM are elaborately introduced to allow practitioners to understand better the concepts and practical implementations. Their performances in model updating and damage detection are compared through three different engineering applications with increased complexity, e.g., a forty-story shear building, a two-span continuous steel beam, and a large-scale steel pedestrian bridge.

Human drivers are requested by the Automated Vehicle (AV) to perform takeover actions if needed. Existing research mainly focuses on predicting the takeover quality due to distraction using wearable sensor data. It is unrealistic, unnatural, and inapplicable to require human drivers to wear these sensors when driving an AV so that their situational awareness for takeover actions can be continuously monitored. Moreover, traffic conflicts can be observed even if drivers take over as requested. Current practice mainly develops conflict-actuated collision avoidance systems that alert drivers once the traffic conflict reaches a certain threshold. There is a research need to anticipate conflicts other than by measuring them. Besides, drivers are still responsible for responding to the alerts, which leaves the possibility of resulting in human error-related safety issues. This research aims at developing a Non-intrusive, Ultra-advanced Collision Avoidance System (NIUCAS) under automated driving. NIUCAS applies the brake pedal for drivers if it predicts the absence of takeover actions due to distraction or predicts traffic conflicts before they can be measured. The NIUCAS prototype was implemented in a driving simulator. An experiment was conducted by recruiting sixty participants to drive a vehicle under Level 3 automation, going through jaywalking scenarios, and being requested to take over. Participants’ demographics were collected to predict the takeover actions, while vehicle-related performance was collected to predict the traffic conflicts. Three machine learning-based modeling techniques were chosen as candidates for predictions. Additionally, an empirical equation is formulated to quantify the safety benefits of implementing NIUCAS.

Automated vehicle (AV) technology is expected to alter travel behavior and residential location choices, yet the psychological motivations behind relocation decisions under current partial automation (Level 2) remain underexplored, as most studies focus on fully autonomous scenarios. This study explores why individuals might relocate in response to AV availability in both short-term and long-term contexts and predicts how willingness to relocate changes as automation levels advance. In a survey of Kentucky residents, data were collected on demographic and economic characteristics, travel needs, built environment attributes, AV familiarity, comfort with different automation levels, and willingness to relocate if AVs were available. Multiple machine learning models with Shapley Additive Explanations (SHAP) were used to predict and interpret changes in relocation willingness. Results indicate that greater comfort with high-level automation and higher AV familiarity increase relocation intentions, particularly among men, older adults with higher incomes, and urban residents. SHAP analysis reveals that built environment, age, and comfort with fully autonomous driving are the most influential predictors of changes in relocation willingness. Findings inform land use and housing policy by identifying where perception-driven relocation pressures are likely to emerge and by outlining adaptive tools to guide spatial growth as AV technology advances.

This article addresses the stop-go dilemma at four-quadrant gate (4QG) highway-rail crossings (HRCs) by introducing a novel reinforcement learning (RL) and Dilemma Field (DF) framework to minimize driver uncertainty. Conventional Type I and Type II dilemma-zone definitions rely on rigid spatial boundaries, risking over-protection or under-protection when driver behaviors deviate from assumed norms. In contrast, the proposed DF theory represents uncertainty as a continuous probability distribution, capturing the reality that vehicles beyond a nominal zone may remain partially confused while some within a zone might be decisive. A Markov chain stability theorem based on the Foster–Lyapunov criterion demonstrates that reducing the aggregate probability of uncertainty drives near-exponential convergence to safer, more stable traffic flow. To leverage these insights in real time, a Double Deep Q-Network (Double DQN) agent integrates the DF metric into its state and reward functions to adaptively optimize the warning signal activation at 4QG HRCs. Simulation results demonstrate that the proposed method raises minimum time-to-collision margins by 61% and reduces late-amber passing rates by 62% compared to constant-warning-time systems. These findings confirm that a probabilistic treatment of uncertainty, coupled with a learning-based control policy, yields substantially improved safety outcomes.