Ruben Van Coile

This article investigates how the discipline of Law and Economics (“L&E”) can guide legal frameworks for fire safety by aligning legal rules, economic incentives, and engineering practices. We first introduce core L&E concepts – externalities, information asymmetries, public-goods, and liability structures – and their relevance for fire safety. We then develop a mathematical model for the total cost of fire (for a design problem or building portfolio), and use this model to contrast prescriptive and performance-based regimes and compare three liability standards (strict liability, code-compliance negligence, and due-diligence negligence). When considering a full internalization of the cost of fire (i.e., when the private decision-maker carries all fire-related costs), our analyses show that prescriptive regimes generate over- or under-investment in preventive measures, resulting in sub-optimal societal outcomes. Performance-based regimes, on the other hand, provide private decision makers with the incentive to invest up to societally optimal safety investments. The comparison of liability regimes shows that the internalization of the cost of fire is most effectively achieved by the strict liability regime. A liability regime of negligence based on due diligence (professional standards) also pushes private decision-makers towards adopting societally optimum designs. However, when negligence is established by the (lack of) compliance with prescriptive fire safety codes, this results in societally suboptimal designs. These results imply that policymakers should favor adaptable, efficiency-oriented regulatory designs, and highlight that liability regimes should be calibrated to internalize fire safety related costs. In more general terms, the results demonstrate the power of L&E approaches for supporting discussions on fire safety legislation.

Current standards suggest characterizing the distribution of glass fracture strength using co-axial double-ring standardized tests. While several international standards do not specify a minimum test number, ASTM C1499-19 recommends conducting at least 30 valid tests (defined as tests where fracture initiates in the inner ring). In practice, the results of invalid tests are often discarded. However, the invalid tests still hold information on the fracture strength, and neglecting them can lead to a biased estimation of the latter. This study proposes a Bayesian inference approach to extract and incorporate such information in glass strength characterization. The approach is applied to existing datasets at ambient and elevated temperatures, examining how the test number influences the coefficient of variation of the characteristic fracture strength used for design. The results show that discarding invalid tests may bias glass strength estimates. Furthermore, for the considered dataset at elevated temperatures, incorporating invalid tests enables achieving the same precision level as implied by the standard with fewer tests. These results show that the proposed approach can improve testing efficiency and refine characteristic fracture strength predictions. Finally, the study quantifies the impact of incorporating invalid tests on the structural reliability of a slab under different load ratios. Results show that ignoring invalid tests leads to an overestimation of the reliability index. This finding highlights that invalid tests should be considered for more robust reliability evaluations of glass structures.

The increasing complexity in the Fire Safety Engineering (FSE) field requires the adoption of risk assessment and safety management methods which address such complex behavior. In this context, complexity refers to systems of multiple interacting components where non-linear and adaptive relationships generate emergent behaviors, i.e., outcomes that cannot be anticipated solely by examining the individual parts. Systems Thinking (ST), including tools such as System Archetypes, Causal Loop Diagrams, and System Dynamics (SD) modeling, offers a holistic framework to address these challenges. This article presents a literature review on the application of ST and SD in FSE, focusing on its use in enhancing fire safety for buildings and infrastructures by identifying key trends, methodologies, challenges, and future research directions. A six-stage framework is adopted for the literature review which examines the development of ST in FSE. In total 35 studies were found as relevant for the FSE field due to their application of at least one ST tool. However, challenges such as the complexity of modeling large-scale systems, the need for high-quality data, and the integration of SD with other fire safety engineering methods remain. Overall, this review underscores the value of ST as a powerful tool for addressing the complexities of FSE, testing the effectiveness of different safety measures, and improving risk assessment in various environments, while highlighting that its potential usage has not been fully developed yet.

A framework of “Adaptive Fire Testing” for knowledge-driven testing is being developed at Ghent University. When applying this framework, test specifications are adapted to maximize the expected utility (e.g., information gain) of performing the test. Thus, the value of conducting a test or experiment is evaluated before its execution, and a rational choice can be made between test protocol alternatives. In this contribution, we showcase a comparison among competing testing protocols based on expected information gain, using closed-form expressions and without relying on advanced sampling techniques. The case study highlights the potential of Adaptive Fire Testing to transform fire safety science and engineering by providing an objective and data-driven approach to choose between alternative experimental protocols.

Surrogate modelling is increasingly used in engineering to improve computational efficiency in complex simulations. However, traditional data-driven surrogate models often face limitations in generalizability, physical consistency, and extrapolation—issues that are especially critical in safety-sensitive fields such as fire safety engineering (FSE). To address these concerns, physics-informed surrogate modelling (PISM) integrates physical laws into machine learning models, enhancing their accuracy, robustness, and interpretability. This systematic review synthesises existing applications of PISM in FSE, classifies the strategies used to embed physical knowledge, and outlines key research challenges. A comprehensive search was conducted across Google Scholar, ResearchGate, ScienceDirect, and arXiv up to May 2025, supported by backward and forward snowballing. Studies were screened against predefined criteria, and relevant data were analysed through narrative synthesis. A total of 100 studies were included, covering five core FSE domains: fire dynamics, wildfire behaviour, structural fire engineering, material response, and heat transfer. Four main strategies for embedding physics into machine learning were identified: feature engineering techniques (FETs), loss-constrained techniques (LCTs), architecture-constrained techniques (ACTs), and offline-constrained techniques (OCTs). While LCT and ACT offer strict enforcement of physical laws, hybrid approaches combining multiple strategies often produce better results. A stepwise framework is proposed to guide the development of PISM in FSE, aiming to balance computational efficiency with physical realism. Common challenges include handling nonlinear behaviour, improving data efficiency, quantifying uncertainty, and supporting multi-physics integration. Still, PISM shows strong potential to improve the reliability and transparency of machine learning in fire safety applications.