• 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.

Civil engineering has only recently started the digitalisation journey by standardising around Building Information Models (BIMs). In the process of construction a dimension of time is added in what is called 4D BIM and this can serve as the basis for a digital twin. It is predicted that such a digital twin can enhance the overall overview of status of the construction of a new building by means of different types of sensors, and interpreting these in relation to a BIM. In the construction phase there are rules and regulations targeting the safety of the different kinds of construction workers at the construction site. In this paper we provide a vision of how digital twins can assist with spotting potential violations of the constraints stated by the rules and regulations, and empirically evaluate a proof-of-concept software tool on a large scale, real-world 4D BIM.

We present a new approach based on spatial reasoning in Answer Set Programming (ASP), and a prototype software tool, for automatically evaluating construction safety compliance of real-world Building Information Models (BIM) that have both a geometric component and temporal component in the form of a construction plan and schedule (4D BIM). In the 4D BIM domain, geometries of building objects are large and complex making it highly impractical to represent geometries as ASP facts, unoptimised spatial reasoning can be prohibitively slow, and rounding errors in floating point arithmetic often result in logical contradictions. Our novel framework addresses these challenges by integrating a specialised geometry database, built-in spatial optimisations, and support for real arithmetic solving. We empirically evaluate our prototype software tool on two large 4D BIM models from real buildings to demonstrate the practicality and scalability of our new framework to real-world workplace hazard prevention tasks in construction safety-in-design analysis.

We present a framework and proof-of-concept implementation for functional spatial reasoning within high-order logic programming. The developed approach extends lambdaProlog to support reasoning over spatial variables via Constraint Handling Rules. We implement our approach within Embeddable lambdaProlog Interpreter (ELPI) and demonstrate key features from combined reasoning over spatial functions and relations. The reported research is an ongoing development of the declarative spatial reasoning paradigm.

We propose a hybrid architecture for systematically computing robust visual explanation(s) encompassing hypothesis formation, belief revision, and default reasoning with video data. The architecture consists of two tightly integrated synergistic components: (1) (functional) answer set programming based abductive reasoning with space-time tracklets as native entities; and (2) a visual processing pipeline for detection based object tracking and motion analysis. We present the formal framework, its general implementation as a (declarative) method in answer set programming, and an example application and evaluation based on two diverse video datasets: the MOTChallenge benchmark developed by the vision community, and a recently developed Movie Dataset.

Parametric design is an established method in engineering and architecture facilitating the rapid generation and evaluation of a large number of configurations and shapes of complex physical structures according to constraints specified by the designer. However, the emphasis of parametric design systems, particularly in the context of architectural design of large-scale spaces, is on numerical aspects (e.g., maximising areas, specifying dimensions of walls) and does not address human-centred design criteria, for example, as developed from behavioural evidence-based studies. This paper aims at providing an evidence-based human-centred approach for defining design constraints for parametric modelling systems. We determine design rules that address wayfinding issues through behavioural multi-modal data analysis of a wayfinding case study in two healthcare environments of the Parkland hospital (Dallas). Our rules are related to the environmental factors of visibility and positioning of manifest cues along the navigation route. We implement our rules in FreeCAD, an open-source parametric system.

Abstract The systematic modelling ofdynamic spatial systemsis a key requirement in a wide range of application areas such as commonsense cognitive robotics, computer-aided architecture design, and dynamic geographic information systems. We present Answer Set Programming Modulo Theories (ASPMT)(QS), a novel approach and fully implemented prototype for non-monotonic spatial reasoning — a crucial requirement within dynamic spatial systems — based on ASPMT. ASPMT(QS) consists of a (qualitative) spatial representation module (QS) and a method for turning tight ASPMT instances into Satisfiability Modulo Theories (SMT) instances in order to compute stable models by means of SMT solvers. We formalise and implement concepts of default spatial reasoning and spatial frame axioms. Spatial reasoning is performed by encoding spatial relations as systems of polynomial constraints, and solving via SMT with the theory of real non-linear arithmetic. We empirically evaluate ASPMT(QS) in comparison with other contemporary spatial reasoning systems both within and outside the context of logic programming. ASPMT(QS) is currently the only existing system that is capable of reasoning about indirect spatial effects (i.e., addressing the ramification problem), and integrating geometric and QS information within a non-monotonic spatial reasoning context.

The evidence-based analysis of people's navigation and wayfinding behaviour in large-scale built-up environments (e.g., hospitals, airports) encompasses the measurement and qualitative analysis of a range of aspects including people's visual perception in new and familiar surroundings, their decision-making procedures and intentions, the affordances of the environment itself, etc. In our research on large-scale evidence-based qualitative analysis of wayfinding behaviour, we construe visual perception and navigation in built-up environments as a dynamic narrative construction process of movement and exploration driven by situation-dependent goals, guided by visual aids such as signage and landmarks, and influenced by environmental (e.g., presence of other people, time of day, lighting) and personal (e.g., age, physical attributes) factors. We employ a range of sensors for measuring the embodied visuo-locomotive experience of building users: eye-tracking, egocentric gaze analysis, external camera based visual analysis to interpret fine-grained behaviour (e.g., stopping, looking around, interacting with other people), and also manual observations made by human experimenters. Observations are processed, analysed, and integrated in a holistic model of the visuo-locomotive narrative experience at the individual and group level. Our model also combines embodied visual perception analysis with analysis of the structure and layout of the environment (e.g., topology, routes, isovists) computed from available 3D models of the building. In this framework, abstract regions like the visibility space, regions of attention, eye movement clusters, are treated as first class visuo-spatial and iconic objects that can be used for interpreting the visual experience of subjects in a high-level qualitative manner. The final integrated analysis of the wayfinding experience is such that it can even be presented in a virtual reality environment thereby providing an immersive experience (e.g., using tools such as the Oculus Rift) of the qualitative analysis for single participants, as well as for a combined analysis of large group. This capability is especially important for experiments in post-occupancy analysis of building performance. Our construction of indoor wayfinding experience as a form of moving image analysis centralizes the role and influence of perceptual visuo-spatial characteristics and morphological features of the built environment into the discourse on wayfinding research. We will demonstrate the impact of this work with several case-studies, particularly focussing on a large-scale experiment conducted at the New Parkland Hospital in Dallas Texas, USA.

Evidence-based design (EBD) for architecture involves the study of post-occupancy behaviour of building users with the aim to provide an empirical basis for improving building performance [Hamilton and Watkins 2009]. Within EBD, the high-level, qualitative analysis of the embodied visuo-locomotive experience of representative groups of building users (e.g., children, senior citizens, individuals facing physical challenges) constitutes a foundational approach for understanding the impact of architectural design decisions, and functional building performance from the viewpoint of areas such as environmental psychology, wayfinding research, human visual perception studies, spatial cognition, and the built environment [Bhatt and Schultz 2016].

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.

We present a formal framework and implementation for declarative spatial representation and reasoning about the topological relationships between boolean combinations of regions (i.e., union, intersection, difference, xor). Regions of space here correspond to arbitrary axis aligned n-polytope objects, with geometric parameters either fully grounded, partially grounded, or completely unspecified. The framework is implemented in the context of CLP(𝒬𝒮)CLP(𝒬𝒮): A Declarative Spatial Reasoning System. www.spatial-reasoning.com

We propose a human-centred model for abstraction, modelling and computing in function-driven spatial design for architecture. The primitive entities of our design conception ontology and computing framework are driven by classic notions of `structure, function, and affordance' in design, and are directly based on the fundamental human perceptual and analytical modalities of visual and locomotive exploration of space. With an emphasis on design semantics, our model for spatial design marks a fundamental shift from contemporary modelling and computational foundations underlying engineering-centred computer aided design systems. We demonstrate the application of our model within a system for human-centred computational design analysis and simulation. We also illustrate the manner in which our design modelling and computing framework seamlessly builds on contemporary industry data modelling standards within the architecture and construction informatics communities.

A central role of Geographic Information Systems (GIS) is to allow the identification and visualisation of relevant spatial features from typically large volumes of data. This requires a querying system to provide both flexibility and usability. While standard GIS querying capabilities are often either very limited, or require a user to have knowledge in specialised areas, techniques in qualitative spatial reasoning have been developed that provide a powerful and intuitive method of representing and reasoning about spatial information. In this paper we present a method for querying about the qualitative distance between features using dynamic networks such as roads, bus or ferry services, and flight paths, rather than only using the Euclidean distance. Linguistic values are used to implement qualitative distances for the linguistic variable proximity. TreeSap GIS with qualitative querying support is presented to demonstrate how qualitative distance measures through a network can provide both a computationally practical solution and a mechanism through which non-experts benefit from powerful search tools.