Ao Du

This study develops a novel artificial intelligence (AI)-based methodology to enable risk-based, long-term-focused decision support for bridge asset management. This methodology features three key modules: (1) a bridge condition simulation module integrating Markovian state transition dynamics, (2) a lifecycle cost analysis module to holistically quantify and aggregate the direct and indirect costs incurred due to bridge condition deterioration and intervention actions over a prolonged planning horizon, and (3) an AI-based decision-support module for optimal sequential maintenance actions at individual bridge level. The proposed methodology can flexibly accommodate the decision needs of different individual bridges within a bridge portfolio under various sources of uncertainty in near real time. Compared with traditional reactive, condition-based decision policies, the proposed AI-based policies are found to offer more proactive and adaptive decisions and to demonstrate superior performance in minimizing the life cycle costs. To further enhance risk-averse stakeholders’ trustworthiness in the AI-based decision policy, practical action constraints and decision reasoning are introduced, and the effect of action override is also investigated.

This paper presents the design and development of an open-source browser-based interface for coordinating a multi-robot system for on-site construction inspections. The developed system enables remote human operators to effectively oversee and direct automated inspection tasks using both aerial and ground-based robotic platforms. The interface incorporates gaze-tracking technology for intuitive target selection and implements a containerized microservice architecture to ensure reliable deployment across different operating environments. The complete system architecture, implementation methodology, and a real-world inspection scenario are presented. The application has been released as open-source software to encourage adoption and further development by the construction automation community.

Highway bridges are fundamental components of transportation networks. Although chronic deterioration of bridge conditions has been the primary focus of bridge asset management, the implications of seismic hazards, as well as their interaction with bridge maintenance and seismic retrofitting strategies has yet to be fully studied. This study aims to quantitatively assess the long-term performance and cost effectiveness of various seismic retrofitting options when coupled with artificial intelligence (AI)–based maintenance decision policies. To achieve this, bridge component deterioration is modeled using Markov processes. To account for seismic hazard threats, bridge system-level seismic fragility and risk quantification modules are introduced to estimate seismic damage probabilities. The analysis also includes a comprehensive evaluation of both direct and indirect costs associated with maintenance and retrofitting actions for life-cycle cost estimations. Next, the developed bridge performance and life-cycle cost models are coupled with a powerful AI approach known as deep reinforcement learning (DRL) to provide a comprehensive bridge management policy. Finally, the study presents an AI-based maintenance policy integrated with seismic retrofitting strategies and compared with condition-based maintenance policy. The findings underscore the benefits of a holistic AI-driven decision-making process that can manage both routine maintenance and seismic repair actions. Moreover, the evaluation of seismic retrofitting within an AI-driven maintenance framework highlights the long-term benefits of such integration, especially for high seismicity regions.

Robotics has attracted broad attention as an emerging technology in construction to help workers with repetitive, physically demanding, and dangerous tasks, thus improving productivity and safety. Under the new era of human–robot coexistence and collaboration in dynamic and complex workspaces, it is critical for robots to navigate to the targets efficiently without colliding with moving workers. This study proposes a new deep reinforcement learning (DRL)–based robot path planning method that integrates the predicted movements of construction workers to achieve safe and efficient human–robot collaboration in construction. First, an uncertainty-aware long short-term memory network is developed to predict the movements of construction workers and associated uncertainties. Second, a DRL framework is formulated, where predicted movements of construction workers are innovatively integrated into the state space and the computation of the reward function. By incorporating predicted trajectories in addition to current locations, the proposed method enables proactive planning such that the robot could better adapt to human movements, thus ensuring both safety and efficiency. The proposed method was demonstrated and evaluated using simulations generated based on real construction scenarios. The results show that prediction-based DRL path planning achieved a 100% success rate (with a total of 10,000 episodes) for robots to achieve the destination along the near-shortest path. Furthermore, it reduced the collision rate with moving workers by 23% compared with the conventional DRL method, which does not consider predicted information.

Finite element (FE) models that simulate wave–superstructure interactions with the coupled Eulerian–Lagrangian (CEL) technique provide a viable and economical solution to estimate wave impact forces on bridge superstructures. One of the main drawbacks of CEL FE models is that they produce solutions distorted by numerical artifacts with very high frequencies that make it difficult to quantify the magnitude of hydrodynamic forces on superstructures. This paper investigated digital filter parameters for horizontal forces extracted from CEL FE models. The optimal filter configuration was evaluated by comparing unfiltered and filtered horizontal force signals with experimentally measured values from a reduced-scale superstructure specimen tested at the O.H. Hinsdale Wave Research Laboratory at Oregon State University. It was found that digital filters with cutoff frequencies below the fundamental frequency of the superstructure produced the best results and that optimizing Eulerian–Lagrangian surface interactions significantly improved the quality of the calculated force signals.