The Failure Mode and Effects Analysis (FMEA) is paramount for analytical skills of reliability design in dynamic prevention. The FMEA model is a significant method which can simultaneously reduce the operating errors or delays as well as improve the construction quality. In particular, the Risk Priority Number (RPN) in the FMEA model is a vital tool which helps construction managers prioritize problem-solving. As the Internet of Things and big data analytical skills have become progressively widespread and mature, among the three risk indicators of RPN, the number of operating errors or delays per unit time can be estimated by the data collected from the analysis of statistical methods and regarded as the basis of 10-level classification. In addition, when the loss is larger, then the severity is higher. This paper proposed three evaluation criteria, including Occurrence, Severity, and Detection of RPN in construction engineering, and a 10-level classification model. To assist the construction managers, priority for construction improvement can be identified based on RPN calculations.

Traditional engineering risk management has been unable to adapt to the complexity and variability due to its constituent elements and dynamic nature of internal and external environments. Vulnerability, as a concept closely related to risk, has been neglected in the traditional risk management due to its hidden characteristics. This study attempts to quantify and evaluate vulnerabilities of complex engineering projects independently and explore the transmission mechanism between risk and vulnerability factors. Twenty different types of large-scale engineering projects in China were selected as case studies from the Mega Project Case Study Center (MPCSC) of Tongji University. Vulnerability and risk factors of each project were identified and analysed. A mechanism model was developed to explore the impacts of vulnerabilities and risks through ta Fuzzy Petri Net. Four main vulnerability-risk critical paths were identified through the reverse labelling method. The overall evaluation of engineering project risks considering the impacts of vulnerabilities is the highlight of this paper. This study interprets the cognition and evaluation of complex engineering risks from a new perspective, enriches the connotation of engineering risk management, and provides a reference for risk management and decisionmaking of complex engineering projects.

A digital-enabled safety management approach is increasingly crucial for crane operations, which are common yet highly hazardous activities sensitive to environmental dynamics on construction sites. However, there exists a knowledge gap regarding the current status and developmental trajectory of this approach. Therefore, this paper aims to provide a comprehensive overview of digital technologies for enhancing crane safety, drawing insights from articles published between 2008 and 2021. Special emphasis is placed on the sensing devices currently in use for gathering “man-machine-environment” data, as well as the communication networks, data processing algorithms, and intuitive visualization platforms employed. Through qualitative and quantitative analysis of the literature, it is evident that while notable advancements have been made in digital-enabled crane safety management, these achievements remain largely confined to the experimentation stage. Consequently, a framework is proposed in this study to facilitate the practical implementation of digital-enabled crane safety management. Furthermore, recommendations for future research directions are presented. This comprehensive review offers valuable guidance for ensuring safe crane operations in the construction industry.

Roads and highways are often managed by using Geographic Information Systems (GIS). However, these systems lack the level of detail that Building Information Modelling (BIM) can bring to an infrastructure management system. BMGIS integration allows the management of information from both infrastructure and environmental points of view. This provides an overview of the infrastructure, facilitating decision-making process throughout its complete life cycle. This article shows a semi-automated process to generate the hybrid BIM-GIS model of Madrid Calle30. The model together with an external database was uploaded to an intelligent management platform that allows visualising the available documentation, assisting management, and bringing the ring-road closer to a digital twin of the infrastructure.

Construction industry workers; are exposed to serious safety and health risks, hazardous work environments, and intense physical work. This situation causes fatal and non-fatal accidents, reduces productivity, and causes a loss of money and time. Construction safety management can use wearable sensors to improve safety performance. Since there are many types of sensors and not all sensors can be used in construction applications, it is necessary to identify suitable and reliable sensors. This requirement causes a sensor selection problem. The study aims to determine the priority order of physiological and kinematic sensors in preventing risks in the construction industry. Within the scope of this purpose, five criteria and seven alternatives were determined in line with the literature research and expert opinions. The criteria weights were calculated with the AHP method, and the alternatives were ranked with PROMETHEE and AHP. Providing a proactive approach to the use of sensors in the construction industry will provide safer working conditions, identify workers at risk, and help identify and predict potential health and safety risks. It will contribute to the literature on improving construction health and safety management.

In the context of sustainable buildings, an ecological study of building and insulating materials is critical since it may assist affirm or shift the path of new technology development. Utilising sustainable material is a part of the sustainable improvement. For this reason, material fabrication is the primary process for the energy usage and release of intense environmental gaseous. The fabrication of the insulation and building materials, as in every fabrication process, comprises an energy consumption of crude materials in addition to the pollutants’ release. In buildings, insulation is a relevant technological resolution for cutting energy usage. This study aims to assess the primary energy consumption and the environmental effects of the fabrication of building and thermal isolation materials by using a new hybrid MCDM model. The proposed new hybrid MCDM model includes Fuzzy FUCOM, CCSD and CRADIS methods. While the subjective weights of the criteria were determined with the fuzzy FUCOM method, the objective weights of the criteria were determined with the CCSD method. Construction materials were listed with the CRADIS method. According to the fuzzy FUCOM method, the most important criterion was determined as the CR3 criterion, while the most important criterion according to the CCSD method was determined as the CR1 criterion. According to the combined weights, the most important criterion was determined as the CR3 criterion. According to the CRADIS method, the material with the best performance was determined as Cement Plaster. The methodology used in this study is a novel approach therefore it has not been used in any study before. In addition, since the CRADIS method is a newly developed MCDM method, the number of articles related to this method is low. Therefore, this research gap will be filled with this study.

The construction and management of large-scale projects have the characteristics of complexity, dynamic and offline, and how to evaluate it is a research problem accurately. This study addresses this question through multidisciplinary cross-applied research. The research analyses and optimizes the environmental impact of the construction stage of superlarge bridges by establishing a theoretical model system of environmental impact resilience. The analysis shows that industrialized construction can save 56.31% of materials compared with traditional construction but increase the consumption of machinery and personnel by 11.18%. Ultimately, environmental pollution can be significantly reduced. This study breaks through the difficulty of accurately evaluating discrete dynamic factors. It has realized the application of multidisciplinary research to solve management optimization and design problems in the elastic and dynamic changes of super-large bridges during construction. This research provides rich theoretical models and advanced analytics experience data for environmental resilience impacts and project resilience management models, laying a solid scientific foundation for dynamic control and sustainable development assessment of statically indeterminate structures in the future.

Construction project complexity can be daunting, so both academics and practitioners have been looking for guidance. Previous studies have attempted to reconcile the inconsistencies and complexities in the relationships among project complexity, project success, and project management success. However, such research has failed to establish these clear relationships. Accordingly, the approach of systematic review and meta-analysis is applied in this study to investigate and compare how different project complexity affects project success and project management success by selecting 22 articles and 77 effect sizes. The results indicate that integrational complexity significantly positively affects project success, whereas it is not significantly negatively associated with project management success. Within a technical-organizational-environmental (TOE) framework, effects of organizational, environmental, and technical complexity on project success and project management success are also discussed here. A possible moderator (the national/regional income level) is tested and verified. The findings contribute to the system of knowledge on project complexity and provide guidelines for decision-makers to achieve a balance between project success and project management success in routine operation of construction projects.

Frost resistance in very cold areas is an important engineering issue for the durability of concrete, and the efficient and accurate prediction of the frost resistance of concrete is a crucial basis for determining reasonable design mix proportions. For a quick and accurate prediction of the frost resistance of concrete, a Bayesian optimization (BO)-random forest (RF) approach was used to establish a frost resistance prediction model that consists of three phases. A case study of a key national engineering project results show that (1) the RF can be used to effectively screen the factors that influence concrete frost resistance. (2) R2 of BO-RF for the training set and the test set are 0.967 and 0.959, respectively, which are better than those of the other algorithms. (3) Using the test data from the first section of the project for prediction, good results are obtained for the second section. The proposed BO-RF hybrid algorithm can accurately and quickly predict the frost resistance of concrete, and provide a reference basis for intelligent prediction of concrete durability.

To promote consumers to buy green housing, the paper tries to find the effect of information on consumers’ green housing purchasing behavior. It classifies the contents and providers of information and explores their different influences. The results show that: 1) Consumers’ age and environmental protection attitude have a significant impact on their purchasing behavior, while consumers’ gender and monthly income have no significant impact; 2) Consumers are more affected by information relating to the economy and indoor air quality. The detailed order of influence is as follows: information on loan at concessionary rates > cost saving in usage phase > indoor air quality > waste classification > investment benefit > carbon emission reduction > thermal comfort > acoustic environment > greening rate > luminous environment; 3) Consumers with higher environmental awareness care more about the information on living environment and carbon emission. Whereas, those with low awareness of environmental protection are more affected by information on economic benefits. 4) Regarding different information providers, the consumers are mostly impacted by the government, while the information from developers could induce limited effects. It could guide the government and developers to provide appropriate information to promote GH purchasing behavior.