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Manual building code compliance checking is a time-consuming, labor-intensive and error-prone process. Automated logic-based reasoning is an essential step in the automation of this process. There have been previous studies using logic programming languages for automated logic-based reasoning to support automated compliance checking (ACC) of building designs with building codes. As a high-performance implementation of the standard logic programming language, B-Prolog was widely used in these studies. However, due to the support of dynamic predicates and user-defined operators, the predicates’ functions vary according to different user definitions; therefore, B-Prolog is sometimes not reliable for building code reasoning. As a more expressive, scalable, and reliable alterative to B-Prolog, Picat, a logic-based multi-paradigm programming language, provides a new and potentially more powerful platform for automated logic-based reasoning in ACC. To explore the potential value of Picat in ACC, in this study, the authors compared Picat and B-Prolog performance in automatically checking 20 requirement rules in the 2015 International Building Code. The experimental results showed that the automated checking for building codes in the B-Prolog version was faster than that in the Picat version, whereas the Picat version was more reliable than the B-Prolog version. This could be the result of B-Prolog using unifica-tion and Picat using pattern matching for indexing rules. More potential applications of Picat in ACC domain need further research. Furthermore, this schema could be used in the teaching of ACC to graduate construction students, illustrating the need to focus on the reliability, predictability and scalability of the process, in order to provide a practical solution to improving code compliance checking processes. 

Thin-walled structures have been widely used in automotive and aerospace industries to improve the system crashworthiness and impact protection. However, during manufacturing, transporting and handling processes, initial geometric imperfections are inevitably introduced to the thin-walled structures, which imposes negative impacts to the mechanical performance and service life of the thin-walled structures. In this study, we have introduced structural imperfection with controlled geometry and dimension to thin-walled steel tubes and characterized the mechanical response of these empty tubes and LN-filled tubes by quasi-static compression tests. Results show, the structural imperfection reduces the energy absorption capacity of empty tubes by about 20%. As the tube is filled with LN, the structural imperfection does not affect the energy absorption capacity of LN filled tube. The enhanced imperfection resistance is attributed to the suppression of imperfection growth caused by the strong liquid-solid interaction between the LN and tube wall. These findings suggest that the LN filling material can effectively reduce the adverse impact of structural imperfection and shed light on future design of thin-walled energy absorption devices. 

Conventional reinforcement learning (RL) allows an agent to learn policies via environmental rewards only, with a long and slow learning curve, especially at the beginning stage. On the contrary, human learning is usually much faster because prior and general knowledge and multiple information resources are utilized. In this paper, we propose a PlannerActor-Critic architecture for huMAN-centered planning and learning (PACMAN), where an agent uses prior, high-level, deterministic symbolic knowledge to plan for goal-directed actions. PACMAN integrates Actor-Critic algorithm of RL to fine-tune its behavior towards both environmental rewards and human feedback. To the best our knowledge, This is the first unified framework where knowledge-based planning, RL, and human teaching jointly contribute to the policy learning of an agent. Our experiments demonstrate that PACMAN leads to a significant jump-start at the early stage of learning, converges rapidly and with small variance, and is robust to inconsistent, infrequent, and misleading feedback. 

Recent successes of Reinforcement Learning (RL) allow an agent to learn policies that surpass human experts but suffers from being time-hungry and data-hungry. By contrast, human learning is significantly faster because prior and general knowledge and multiple information resources are utilized. In this paper, we propose a Planner-Actor-Critic architecture for huMAN-centered planning and learning (PACMAN), where an agent uses its prior, high-level, deterministic symbolic knowledge to plan for goal-directed actions, and also integrates the Actor-Critic algorithm of RL to fine-tune its behavior towards both environmental rewards and human feedback. This work is the first unified framework where knowledge-based planning, RL, and human teaching jointly contribute to the policy learning of an agent. Our experiments demonstrate that PACMAN leads to a significant jump-start at the early stage of learning, converges rapidly and with small variance, and is robust to inconsistent, infrequent, and misleading feedback.