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.

Rapid reconnaissance of building damage is critical for disaster response and recovery. Drones have been utilized to collect aerial images of affected areas in order to assess building damage. However, there are two challenges. First, processing many aerial images to detect and classify building damage based on a consistent standard remains laborious and complex, necessitating a new automated solution to achieve accurate building damage detection and classification. Second, drone operations during disaster response rely primarily on human operators’ experience and seldom use the obtained building damage information to optimize drone mission planning. Therefore, this study proposes a new method, which automates building damage reconnaissance with drone mission planning for disaster response operations. Specifically, a deep learning method is developed to detect and classify building damages using a newly labeled dataset consisting of 24,496 distinct instances of building damage. This deep learning method is validated, achieving 71.9% mean average precision. In addition, building damage information is modeled and integrated into mission planning, in order to optimize drones’ task assignments and route calculations. A tornado disaster in Tennessee is used as a case study to quantitatively evaluate this methodology. The present study concludes that optimal drone mission planning during disaster response can be augmented using accurate building damage information acquired from deep learning methods.

Public–private partnerships (PPPs) have emerged as a promising alternative to financing infrastructure projects in order to address the infrastructure restoration and rehabilitation challenge. However, in the current practice no unanimous agreement exists on what factors to consider when making capital structure decisions in PPP infrastructure projects. To close this gap, generalized least-squares (GLS) regression analysis was performed on data from 498 projects across 22 countries to identify the capital structure determinants. At the 10% significance level, three project-specific factors (number of sponsors, number of lenders, and contract type) and four country-specific factors (total tax rate, real interest rate, risk premium on lending, and bond market capitalization) were identified as critical. It contributes to the body of knowledge (1) by explaining the financial decision from the lender’s perspective, which complements common capital structure theories that focus on sponsors; and (2) by providing sponsors, lenders, and governments with knowledge on the primary factors that drive decisions, which in turn forms the base for effective negotiation and optimization of the capital structure in PPP infrastructure projects.

Data sharing between different organizations is critical in supporting decision making in civil infrastructure engineering projects (e.g., transportation projects). Understanding the characteristics of data-sharing-related factors in civil infrastructure engineering is crucial for the civil engineering community to identify the priorities in promoting data sharing. The ASCE Data Sensing and Analysis (DSA) Committee initiated an investigation on data-sharing barriers in civil infrastructure engineering. This work produced (1) a classification of data-collection and data-use contexts in the domain of civil infrastructure engineering; (2) a comprehensive classification of data-sharing barriers and data-sharing promotion strategies in this domain; and (3) a synthesis of the opinions about the current practices, future vision, barriers, and data-sharing promotion strategies from civil infrastructure professionals and researchers using an online survey. The survey results revealed that (1) more data-sharing activities are occurring within the organizations, such as academia, government agencies, and for-profit corporations, compared with data exchanges among these communities; (2) “if there is a cost sharing in the data sharing,” is the condition that makes data providers more willing to share data with others; and (3) compared with technology improvements, the protocol of data sharing in the business domain has more potential in promoting data sharing. Meanwhile, this research identified the top five priorities among 20 data-sharing promotion strategies, which are protocol in industry and organization, data-standard regulation, data validity, data security protection, and large-scale data-sharing technology. These five priorities play critical roles in a roadmap showing how policy, business models, and technical solutions should cooperate to promote data sharing in civil infrastructure engineering.