This study unveils causes of accidents in repair, maintenance, alteration and addition (RMAA) work. RMAA work is playing an increasingly important role in developed societies, including Hong Kong. Safety problems associated with RMAA work in Hong Kong has reached an alarming level. In view of rapid expansion of the RMAA sector and rising proportion of accidents in the construction industry, there is a pressing need to investigate causes of RMAA accidents. Structured interviews were conducted with RMAA contractors to explore causes of accidents in the RMAA sector. A two-round Delphi method with 13 safety experts was subsequently employed to verify the interview findings and rank the relative degree of importance for various causes of accidents. Accidents happen in RMAA work due to intersection of reasons. One of the root causes of accidents in RMAA works is low safety awareness of RMAA workers; however, wider organizational and industrial factors are not negligible. This study sheds light on why accidents happen in the RMAA sector. Only when the factors leading to accidents are identified can effective measures be made.

An analysis for the causes of accidents of repair, maintenance, alteration and addition works in Hong Kong

Large Language Models (LLMs) trained on large data sets came into prominence in 2018 after Google introduced BERT. Subsequently, different LLMs such as GPT models from OpenAI have been released. These models perform well on diverse tasks and have been gaining widespread applications in fields such as business and education. However, little is known about the opportunities and challenges of using LLMs in the construction industry. Thus, this study aims to assess GPT models in the construction industry. A critical review, expert discussion and case study validation are employed to achieve the study's objectives. The findings revealed opportunities for GPT models throughout the project lifecycle. The challenges of leveraging GPT models are highlighted and a use case prototype is developed for materials selection and optimization. The findings of the study would be of benefit to researchers, practitioners and stakeholders, as it presents research vistas for LLMs in the construction industry. 

GPT models in construction industry: Opportunities, limitations, and a use case validation

Large Language Models(LLMs) trained on large data sets came into prominence in 2018 after Google introduced BERT. Subsequently, different LLMs such as GPT models from OpenAI have been released. These models perform well on diverse tasks and have been gaining widespread applications in fields such as business and education. However, little is known about the opportunities and challenges of using LLMs in the construction industry. Thus, this study aims to assess GPT models in the construction industry. A critical review, expert discussion and case study validation are employed to achieve the study objectives. The findings revealed opportunities for GPT models throughout the project lifecycle. The challenges of leveraging GPT models are highlighted and a use case prototype is developed for materials selection and optimization. The findings of the study would be of benefit to researchers, practitioners and stakeholders, as it presents research vistas for LLMs in the construction industry.

pdf/gpt-models-in-construction-industry-opportunities-3t48o6d9.pdf

GPT Models in Construction Industry: Opportunities, Limitations, and a Use Case Validation

Failures of water distribution networks (WDNs) are rising at an exponential rate, necessitating immediate attention. An effective way to reduce the failure rate is to develop accurate predictive models for the failure probability of water pipes, which are the most critical assets of WDNs. Despite the fact that researchers have invested efforts to develop various predictive models, the extant literature lacks a complete state-of-the-art review. To fill this gap, this study employs a mixed approach (i.e., quantitative and qualitative) by providing (a) a bibliometric analysis of existing scholarly literature, (b) a systematic review of the techniques used in modeling the failure probability of water pipes, including physical, statistical, and machine-learning (ML)-based models, and (c) identified gaps and future research directions. The bibliometric analysis shows that ML-based models are emerging and, hence understudied as compared to the physical and statistical-based models. Regarding the systematic review, a proper understanding of the development of each model has been provided in addition to their advantages and critiques. Furthermore, failure probability integration methods are discussed. Findings reveal that the social and operation-related predictors have been understudied, thereby suggesting their further exploration. This study adds to the existing body of knowledge by providing water utilities and academics with a comprehensive understanding of the probability of water pipe failure, which will be useful in the decision-making process and network management. 

Toward Sustainable Water Infrastructure: The State‐Of‐The‐Art for Modeling the Failure Probability of Water Pipes

Safety of repair, maintenance, alteration, and addition (RMAA) works have long been neglected because RMAAworks are often minute and only last for a short period of time With rising importance of the RMAA sector in many developed societies, safety of RMAA works has begun to draw attention Many RMAA contracting companies are small- and medium-sized enterprises (SMEs) that do not have
comprehensive safety management systems Existing safety legislation and regulations for new construction sites are not fully applicable to RMAAworks Instead of relying on explicit and well-established safety systems, tacit safety knowledge plays an extremely important role in RMAA projects To improve safety of RMAAworks, safety knowledge should be better managed However, safety knowledge is difficult to capture in RMAA works This study aims to examine safety management practices of RMAA contracting companies to see how safety knowledge of RMAA projects is managed Findings show that RMAA contracting companies undertaking large-scale RMAA projects have more initiatives of safety management Safety management of small-scale RMAA works relies heavily on the motivation of site supervisors
and self-regulation of workers Better tacit knowledge management improves safety performance To enhance safety capability of RMAA contracting companies, a knowledge sharing culture should be cultivated The government should provide assistance to SMEs to implement proper safety management practices in small-sized projects Potentials of applying computer software technology in RMAA projects to
capture, store, and retrieve safety information should be explored Employees should be motivated to share safety knowledge by giving proper recognition to those who are willing to share

Safety management in repair, maintenance, minor alteration, and addition works : knowledge management perspective

The Repair, Maintenance, Minor Alteration, and Addition (RMAA) sector accounted for 50% of all fatalities in the Hong Kong construction industry in 2016. In spite of the different policies launched...

Effectiveness of policies and difficulties in improving safety performance of repair, maintenance, minor alteration, and addition works in Hong Kong

Leakages in water distribution networks (WDNs) cause economic losses and environmental hazards. It is, therefore, unsurprising that water-leak management has been a focus of research over the last couple of decades, but leaks in WDNs still occur frequently. Thus, this domain is experiencing a transformation from traditional signal processing and statistical-based models to artificial intelligence (AI) based models for recognizing complex leak patterns, handling large datasets, and establishing accurate leak-management models, especially in leak detection and localization. However, a comprehensive review of the application of AI in water-leak management is largely missing from the literature. To bridge this gap, this review presents a criteria-based critical review to systematically investigate the existing literature on the application of AI in four sub-domains of leak management including leak detection, localization, prediction, and sizing. The first criterion (research attributes) established the (1) research trends, (2) links between influential countries and sources, and (3) popular keywords using scientometric analysis. The systematic analysis of the second criterion (research technicality) and the third criterion (research focus) revealed the (1) AI-techniques adopted, (2) equipment used for collecting data, (3) data features used in the models, (4) objectives of different models adopted, (5) type of experiments conducted to collect the data, and (6) types of pipes for which models were developed. The study highlighted research gaps, future research directions, and proposed a leak management framework for upcoming AI studies in this domain. This review is intended to serve early researchers by enhancing their understanding of existing research in AI-based leak management as well as seasoned researchers by providing a platform for future research.

Criteria-based critical review of artificial intelligence applications in water-leak management

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Effectiveness of policies and difficulties in improving safety performance of repair, maintenance, minor alteration, and addition works in Hong Kong

Criteria-based critical review of artificial intelligence applications in water-leak management