Factors influencing fire accidents in urban complexes: a combined DEMATEL and ISM study.

Fire outbreaks in urban complexes are a major safety concern worldwide. Therefore, this study aims to examine the critical factors that influence fire accidents and their interaction mechanisms in urban settings. A (software factors, hardware factors, environmental factors, parties and other factors, SHEL) model is developed to identify 15 risk factors in four categories affecting fire incidents in urban complexes. The Decision-making Trial and Evaluation Laboratory method (DEMATEL) and Interpretive Structural Modeling (ISM) are employed to identify the key factors and their interrelationships, using the evaluation metrics of degree of influence, affected degree, centrality, and hierarchical structure. The results show that lack of safety management rules and regulations (S13), poor security awareness (S1), and uncorrected hidden dangers (S11) are the top three critical factors. Based on the hierarchical structure and centrality values, eight critical paths with the highest impact on fires are identified; for instance, Path 39 (comprising, lack of safety management rules and regulations (S13) → lack of fire training and drills (S12) → insufficient security knowledge (S2) → poor security awareness (S1) → poor sense of security responsibility (S3) → uncorrected hidden danger (S11) → inadequate maintenance of fire-fighting facilities (S14) → Accident), which, among all disaster impact paths, has the highest centrality value of 21.8796 (out of a total of 15 factors and total centrality value of 42.9226; Path 39 involves seven factors, but its centrality value accounts for 50.97% of the total). Finally, based on the factor analysis results, suggestions for fire control measures are provided to prevent fire accidents and ensure the safety of people and property.

Exploring the intentional unsafe behavior of workers in prefabricated construction based on structural equation modeling.

Compared with traditional onsite construction, prefabricated construction has a more complex working environment, resulting in more safety risks. While cognitive failure has been identified as a primary cause of intentional unsafe behaviors, there remains a lack of knowledge on the formation mechanism underlying intentional unsafe behaviors among workers in precast construction. Using the Theory of Planned Behavior and the risk preference variable, this study constructs a theoretical model for intentional unsafe behaviors of precast construction workers. Data related to precast construction and safety management activities is collected from 208 frontline workers. Structural Equation Modeling is used to test and modify the theoretical model in order to identify the formation mechanism and pathway underlying intentional unsafe behaviors. The findings show that: (1) workers' perceptual behavior control, behavior and attitude, risk preference, and subjective norms influence their intention to engage in unsafe behavior and subsequently lead to intentional unsafe behavior; (2) the effect of personal risk preference on intentional unsafe behaviors is significant, contributing 7.71% to overall intentional unsafe behavior; and (3) the effects of the observed variables are more evident than the initial theoretical model. The most prominent of these are the effects of task intensity (IBC1), safety equipment (IBC2), worker behavior (IOW1), historical behavior (IBC3), and behavioral belief (BAA3). Finally, comprehensive measures to control the intentional unsafe behaviors of precast construction workers are recommended. The results of this study are useful for reducing the occurrence of intentional unsafe behaviors by workers and reducing the incidence of accidents in a complex manufacturing-oriented construction environment.

The impacts of driving variables on energy-related carbon emissions reduction in the building sector based on an extended LMDI model: a case study in China.

As China's main contributor to energy-related carbon emissions, the building sector in Jiangsu Province generates around 13.58% of the national carbon emissions. However, the influential variables of the energy structure in Jiangsu Province have been little investigated during the past decade. With the increasing emphasis on China's investment in technological innovation and adjustment of its industrial structure, research and development (R&D) has become an inevitable area for carbon emissions reduction. Nevertheless, its role in carbon emissions has rarely been examined. In this research, based on the logarithmic mean Divisia index (LMDI) model, the variables affecting the fluctuation of carbon dioxide emissions in the building sector (CEBS) in Jiangsu Province during 2011-2019 were restructured by introducing technological factors related to the construction industry, including energy structure, energy intensity, R&D efficiency, R&D intensity, investment intensity, economic output, and population engaged in the construction industry. From the results, it can be inferred that (1) energy structure, energy intensity, R&D efficiency, and investment intensity operate as inhibitors in increasing CEBS, and investment intensity exerts a more prominent impact on suppressing the growth of CEBS; (2) R&D intensity, economic output, and population engaged have a promotional effect on the fluctuations of CEBS, among which the first factor most actively promoted the increase in carbon emissions, although its role was negligible for economic output and the population; and (3) R&D efficiency, R&D intensity, and investment intensity are the three most critical variables for influencing the CEBS, but they are volatile. The numerical fluctuation caused by the three factors might be correlated to national and local policy interventions. Finally, policy recommendations are put forward for strengthening the management and minimizing the CEBS in Jiangsu Province.

An integrated framework of ground source heat pump utilisation for high-performance buildings.

CO2 emissions from building operations have increased to their highest level globally, moving away from the Paris Agreement goal of below 2 °C. While geothermal is recognised as a promising renewable source, the lack of an integrated framework guiding investigating ground source heat pumps for building operations, along with the incapability of well-known simulation tools in accurately capturing ground thermal performance, hinders its application. This research aims to unlock ground source heat pumps for building operations through an integrated framework, including an overarching improved U.S. National Renewable Energy Laboratory (NREL) monitoring guideline, a sensor-based monitoring prototype, and a g-function-based simulation approach. This research proposes amendments and improvements to the NREL guideline for monitoring geothermal energy by separating Thermal Energy Net Production from Thermal Energy Gross Production. A state-of-the-art case building located in Melbourne, Australia, housing advanced technologies, including ground source heat pump systems, is used to demonstrate and validate the research framework. A typical winter month in the southern hemisphere, July 2021, is monitored for the ground source heat pump systems designed and used for space heating. The findings reveal that the thermal energy generation during working days in July 2021 is close to the simulation results, with a difference of 2.2% in gross thermal energy production and a difference of 0.92% in inlet temperature. This research develops and validates an integrated approach for evaluating ground source heat pump systems, contributing to the utilisation of geothermal energy for building operations.

Exploring Precursors of Construction Accidents in China: A Grounded Theory Approach.

The implementation of precursor management can improve safety performance of construction projects through effectively managing the correlations between construction accidents and their precursors. However, a system of comprehensive knowledge about what precursors mean within the context of construction safety is still lacking. This study aims to capture the nature of precursors in the construction industry and explore the process of a precursor event evolving into a construction accident to fill this gap. Based on 135 construction accident reports in China, this study adopts grounded theory to identify different types of accident precursors and explore their interactions with the development of the accident. An indicator system of precursors for construction accidents was developed, which included two major categories of precursors: behavioral factors and physical factors and five minor categories of precursors: individual behavior factors, organizational driving factors, objective physical factors, construction environmental factors, mechanical equipment factors. In addition, a precursor management strategy that includes the three stages of identification, response and effectiveness testing was established. The results of the study reveal the correlations between precursors and construction accidents, which can promote construction professionals' better understanding about precursors and improve their capabilities of managing precursors in practice.

Factors driving the implementation of reverse logistics: A quantified model for the construction industry.

In the light of increased environmental concerns and the unsustainability of current construction practices, 'reverse logistics' (RL) has emerged as a remedial strategy, whereby decommissioned buildings are salvaged and returned back through the value chain for recovery, refurbishment and reuse. The drivers that impact the uptake of RL are known, but if sustainability outcomes are to be enhanced, the strength of those drivers must be quantified in order to ascertain where efforts should be focused. This study aims to quantify the effects of known drivers on RL, and in so doing identify action items with the greatest potential to positively improve RL outcomes. RL drivers are culled from extant research, and categorized as economic, environmental, or social forces. A conceptual model is developed and tested against questionnaire results drawn from 49 expert respondents active in the South Australian construction industry. The results are analyzed using structured equation modeling. Economic and environmental drivers, such as the continuing relative high cost of salvaged items, along with expediency of cost, time and quality objectives overshadowing regulatory demands for use of such salvaged items, are shown to predict 34% of the variations in implementing RL. Of particular interest is the finding contradicting previous studies, showing that social drivers, such as perceived benefits from 'going green' had no significant impact. Thus, the road-map to improving RL outcomes lies in reducing costs of salvaged materials, augmenting environmental policies that promoted their use, and to initiate a regulatory framework to generate compliance. This insight will be of interest to industry policymakers and environmental strategists alike.