• Automated safety tools may exceed manual methods but still need industry adoption. • Decision-making in construction is hindered by limited safety insight extraction. • This study utilizes knowledge graph for enhanced safety and planning insights. • The proposed framework and ontology create effective safety knowledge representation. • A case study demonstrates the cross-domain impact of safety and planning decisions. Construction projects and their dynamic and yet hazardous work environments face significant challenges. Despite advancements, many proposed solutions for information extraction and utilization remain impractical due to complexity and lack of interoperability. Information is often siloed in proprietary formats, making it difficult to integrate. This issue is evident in the construction safety domain, where advanced risk analysis tools provide detailed insights to hazards but can be overwhelming. Similar challenges exist in cost estimation, schedule evaluation, progress monitoring, and quality compliance checking. Decision-making in construction scheduling struggles to assess how changes impact site safety due to insufficient information and knowledge extraction capabilities, especially when it comes to cross-domain knowledge extraction. This study aims to make safety information accessible to safety and planning professionals. By leveraging Digital Twins, automated safety analysis, and knowledge representation, we enable decision-makers to gain deeper insights into their domain and understand the interplay between project planning and safety. We propose a framework for knowledge extraction, an ontology for capturing knowledge, and query building blocks to transform natural language questions into actionable queries. These methods are tested in a case study, revealing valuable insights into the cross-domain impact of decisions.

A building occupant’s experiences are not passive responses to environmental stimuli, but are the results of multifaceted, prolonged interactions between people and space. We present a framework and prototype software tool for logically reasoning about occupant perception and behaviour in the context of dynamic aspects of buildings in operation, based on qualitative deductive rules. In particular, we focus on the co-presence of different user groups and the resulting impact on perceptual and functional affordances of spatial layouts by utilising the concept of spatial artefacts. As a first proof of concept of our approach, we have implemented a prototype crowd analysis software tool in our new system ASP4BIM, developed specifically to support architectural design reasoning in the context of public-facing buildings with complex signage systems and diverse intended user groups. We evaluate our prototype on the Urban Sciences Building at Newcastle University, a large, state-of-the-art living laboratory and multipurpose academic building. Our findings are that the ASP4BIM-based prototype supports a range of novel query services for formally analysing the impacts of crowds on pedestrians that are logically derived through the use of qualitative deductive rules, that complements other powerful crowd analysis approaches such as agent-based simulation.

Specific occupational construction safety, health, and well-being related knowledge and information are scattered and fragmented. Despite technological advancements of information and knowledge management, a link between safety management and information models is still missing. In this paper we present first steps towards a unifying formal (logic-based) domain model of construction safety, called SafeConDM, that consists of: (1) a semantically rich ontology of hazard, safety concepts, and concept relationships that builds on, and integrates with, existing construction safety ontologies and building information models; (2) a set of first-order if-then rules linking construction site states with the potential for specific hazards to occur that we define in a novel way using spatial artefacts. We present a prototype software tool, based on our ASP4BIM tool that implements SafeConDM for construction hazard analysis and safe construction planning decision support, and empirically evaluate our tool on three real-world construction building models.

Sketching as a natural mode for human communication and creative processes presents opportunities for improving human–computer interaction in geospatial information systems. However, to use a sketch map as user input, it must be localized within the underlying spatial data set of the information system, the base metric map. This can be achieved by a matching process called qualitative map alignment in which qualitative spatial representations of the two input maps are used to establish correspondences between each sketched object and one or more objects in the metric map. The challenge is that, to the best of our knowledge, no method for matching qualitative spatial representations suggested so far is applicable in realistic scenarios due to excessively long runtimes, incorrect algorithm design or the inability to use more than one spatial aspect at a time. We address these challenges with a metaheuristic algorithm which uses novel data structures to match qualitative spatial representations of a pair of maps. We present the design, data structures and performance evaluation of the algorithm using real-world sketch and metric maps as well as on synthetic data. Our algorithm is novel in two main aspects. Firstly, it employs a novel system of matrices known as local compatibility matrices, which facilitate the computation of estimates for the future size of a partial alignment and allow several types of constraints to be used at the same time. Secondly, the heuristic it computes has a higher accuracy than the state-of-the-art heuristic for this task, yet requires less computation. Our algorithm is also a general method for matching labelled graphs, a special case of which is the one involving complete graphs whose edges are labelled with spatial relations. The results of our evaluation demonstrate practical runtime performance and high solution quality.