Recent developments in networked and smart sensors have significantly changed the way Structural Health Monitoring (SHM) and asset management are being carried out. Since the sensor networks continuously provide real-time data from the structure being monitored, they constitute a more realistic image of the actual status of the structure where the maintenance or repair work can be scheduled based on real requirements. This review is aimed at providing a wealth of knowledge from the working principles of sensors commonly used in SHM, to artificial-intelligence-based digital twin systems used in SHM and proposes a new asset management framework. The way this paper is structured suits researchers and practicing experts both in the fields of sensors as well as in asset management equally.

Purpose: Construction workers’ safety compliance is attracting considerable critical attention as it plays a decisive role in improving safety on construction sites. This study applied the concept of differentiating safety compliance into deep compliance (DC) and surface compliance (SC) and relied on trait activation theory to investigate the effects of situational awareness (SA) and emotional intelligence (EI) on safety compliance.Design/methodology/approach: Cross-sectional survey data were collected from 239 construction workers in Australia, and these responses were statistically analyzed using the partial least squares structural equation modeling (PLS-SEM) to validate the proposed model.Findings: Results revealed that both EI and SA positively impacted DC and negatively impacted SC. Moreover, SA partially mediated the link between EI and two types of safety compliance (DC and SC). The outcomes showed that construction workers’ ability in regulating their emotions could influence their perception of environmental cues and the effectiveness of safety compliance behavior.Originality/value: This study sheds light on investigating the antecedents of DC and SC from the perspective of trait activation theory. The findings also have practical implications, stating that construction site managers or safety professionals should consider providing training on construction workers’ EI and SA to enhance their willingness to expend conscious efforts in complying with safety rules and procedures, which can lead to improved safety outcomes.

The essence of Australian community buildings’ sustainable management drives through a previously established decision-making structure with four sustainability aspects and accompanying 18 criteria. Informed decisions are supported with a decision-making model that generates sustainability impacts of building components based on this decision-making structure. Building components’ individual impacts can be assigned using a numbering scale incorporated with linguistic terms. However, similar importance given to each aspect or criterion is arguable when the combined effect is considered. Hence, they should be given different weightings and their combination with individual impacts will produce final sustainability impacts. For calculating weightings, the study uses Analytical Hierarchy Process (AHP), widely used technique in Multi Attribute Decision-Making (MADM). The study also conducted an industry-wide questionnaire across Australian local councils because pair-wise comparison data is essential for weighting calculation. This paper presents the survey data and analysis results that captured weightings of sustainability aspects and criteria.

Community buildings such as childcare/ aged care centres, sports pavilions possesses a massive investment in Australian economy built up over generations. From one viewpoint, they are providing essential services to the community and another viewpoint, the delivery of those services is limited by the ongoing degradation of the structural and service components of buildings, the scarcity of resources and the lack of funding by state and federal governments. This poses a huge challenge to Australian local governments to sustainably manage community buildings accountable to them. Reaching the goal takes further away by the absence of a holistic decision-making model for sustainable management of Australian community buildings. This study approaches the problem holistically identifying the sustainable decisions to be made through triple bottom line (environment, economic and social) and functional aspects. Incorporation of the influencing factors of each aspect led to a comprehensive decision-making structure which was the base for developing an effective decision-making model. The design of the model is based on the combination of two analytical methods namely Analytical Hierarchy Process (AHP) and Neuro Fuzzy System (NFS). The final output of the model computes the sustainability index which interprets the total sustainability impact of a given building component. This enables building asset managers to utilise the index to prioritise the maintenance tasks of all building components. The paper encapsulates the results of a case study to explain the outputs of the model and their practical significance. The study previously conducted two industry-wide questionnaire surveys to finalise the attributes of the decision-making structure and the importance of the attributes (weightings).

This systematic review analyses research that introduces commercial design applications that could be adopted for suicide prevention in homes. Furthermore, this literature review captures social, spatial and biophilic design methods to improve wellness in homes using environmental design psychology. Safety and human wellness frame this spatial design research that examines means and access restriction to improve home safety and prevent suicides. Suicide is a growing phenomenon that deserves specific attention to how environments can impact or restrict events. There is a substantial evidence base to evaluate suicide prevention methods used in high-risk environments of health and healing environments, workplaces and incarceration facilities. This review outlines design methods using spatial arrangement and material choices to improve human wellness in homes. The effects of biochemical reactions, such as those studied in toxicology, and stress are considered in this research to suggest material choices and applications in design to improve mental health in homes. Spatial designs for suicide prevention can guide various prevention measures, such as adopting means and access restriction and environmental design methods for wellness and considering impacts during lockdown periods. Environmental design psychology research supplies evidence for improved spatial arrangements in homes, with evidence showing that design applications can restore and improve mental health. This systematic review shows evidence for planning methods to prevent suicides considering both means and access restriction with considerable biochemical impacts from design. Design methods discovered by this systematic review will be considered for future studies and used within economic modelling to demonstrate design guidelines that improve wellbeing and support existing suicide prevention methods for Australian homes.

Incomplete historical records in utility asset datasets frequently introduce left‐truncation bias, leading to distorted reliability estimates and suboptimal asset management decisions. This study presents a systematic framework for validating left‐truncation correction in Weibull reliability analysis using synthetic data representative of electrical utility assets. A controlled dataset of 300 lifetimes (η = 60 years, β = 3.75) was progressively conditioned to reflect staggered entry, right censoring, and left truncation. Standard maximum likelihood estimation (MLE) produced substantial bias ( = 71.96 years, +19.9%), whereas conditional likelihood methods recovered accurate estimates ( = 60.26 years, +0.44%). A Monte Carlo study across 432 parameter combinations spanning decreasing, constant, and increasing hazard regimes confirms that truncation bias arises from the sampling mechanism and is primarily governed by the Weibull shape parameter β. Left‐truncation and right‐censoring (LTRC) correction removes 83% to 99.9% of material bias where the data can support estimation, with recovery accuracy governed by the observed‐failure count; under decreasing hazard the naive estimator exhibits runaway bias while LTRC remains accurate. Results are further validated using anonymised transformer datasets, highlighting material implications for regulatory replacement planning. The proposed framework provides a reproducible benchmark for evaluating reliability estimators and supports the routine adoption of truncation‐aware methods in utility asset management.