Efficient onsite assembly sequence planning and scheduling (ASPS) is crucial for the successful delivery of precast building projects. The manual ASPS process is tedious, error-prone, and sub-optimal. Existing research on its automation lacks in considering real-world constraints and on-site uncertainties, and suffers from high computational burdens. To address this challenge, this paper proposes a novel reinforcement learning (RL) and Monte Carlo sampling (MCS)-based method for automated ASPS. The method utilizes a temporal graph network to create state embeddings, which are then employed by a Proximal Policy Optimization algorithm-based agent to learn the ASPS policy. The agent learns the policy over a distribution of uncertain variables using MCS, with their values randomly sampled at the start of each episode. Validation on a real-world precast building project demonstrates that the proposed method outperforms traditional methods, yielding dominant solutions in 60% of test cases in deterministic and stochastic conditions, while requiring only about one-third of the training time. Future research can explore Pareto front generation and reward engineering to enhance the practical applicability of the proposed method.

Water treatment plants face growing challenges in managing complex infrastructure assets due to fragmented data systems and poor interoperability between design and operational tools. Traditional workflows require manual re-entry of Building Information Modeling (BIM) data into asset management platforms, leading to inefficiencies and information loss. This paper introduces WAM-ONTO, a semantic framework designed to enhance water infrastructure asset management by leveraging building information modeling, ontologies, and rule-based reasoning. WAM-ONTO integrates the Industry Foundation Classes (IFC4) standard with Web Ontology Language (OWL2) to formally represent asset types, lifecycle properties, and spatial-functional relationships. The framework comprises 996 ontology classes structured into twelve domain-specific categories, enriched with 61 object properties and 89 data properties. Semantic reasoning is implemented using SWRL rules and SPARQL queries, enabling automated classification, risk profiling, and maintenance prioritization. The framework was validated through expert interviews with fourteen domain specialists, consistency checks using multiple reasoners, and performance tests demonstrating 94.2% domain coverage and 91% automated classification accuracy on real-world facility data. Expert validation achieved 96% consensus on practical utility and industry alignment. A Clean-in-Place system case study demonstrates WAM-ONTO’s ability to preserve design knowledge during BIM-to-operations transitions while enabling real-time decision support for maintenance planning and risk assessment.

Preparing constraint-free lookahead schedules (LAS) in the assembly stage of dynamic modular construction (MC) projects requires checking space availability for mobile crane operation using heterogeneous, distributed information sources. Current automated crane space evaluation methods rely on centralized information databases, whereas linked data based approaches are limited by insufficient geometric computation capabilities. This study proposes a framework to model and validate the space constraints for mobile crane operations using the semantic web. It starts with developing an ontology to represent crane lifting space requirements on the semantic web. Information sources, including construction site point clouds, 4D building information models, and crane specifications, are semantically interconnected using linked data. Shapes Constraint Language JavaScript Extension performs constraint validation through JavaScript-based mathematical computations utilizing the Separating Axis Theorem and a triangulation-based approach to check space for crane placement and rotation, respectively. Validation on two MC sites demonstrated the framework’s effectiveness in identifying space constraint violations.