Search results

The purpose of this paper is to identify and prioritise the safety and health practices, which are suitable to improve labour productivity in building construction projects, and to investigate the association between company profiles, project characteristics and the safety practices.

The study adopted a mixed-method research design which involves two phases. In Phase I, qualitative data were collected and analysed to identify safety and health practices. In total, 19 experts who have experience in building construction projects were involved in the interviews. During Phase II, quantitative data were collected from contractors that are involved in the delivery of building projects using questionnaires which were administered via face-to-face interviews. After analysing the quantitative data, the safety and health practices were prioritised and the relationships between the practices and company profiles were investigated.

Safety and health policy, safety and health plan and hazard analysis are found to be the three most important practices that can improve productivity in building construction projects. The results of Spearman correlation analysis revealed that the level of implementation of the safety and health practices increases with project costs. Company experience and company size are positively associated with safety and health practices. Project delay is negatively correlated with safety and health practices.

The study provides useful information for international developers and contractors who want to do the construction business in Australia. Furthermore, contractors involved in the construction of building projects in Australia can implement the identified safety and health practices to improve labour productivity.

The main objective of this research is to identify the most important human resource management (HRM) practices, which have the potential to enhance labour productivity using fuzzy synthetic evaluation approach.

The study used a mixed-methods research design in which qualitative data were collected and analysed during Phase I and quantitative data were analysed during Phase II. Nineteen experts who have experience in building construction projects were involved in interviews conducted in Phase I. During Phase II, quantitative data were collected from contractors that were involved in the delivery of building projects using questionnaires and the data were analysed using FSE technique.

Clear delegation of responsibility, stability of organisational structure and crew composition are found to be the three most important HRM practices that can enhance productivity in building construction projects. The findings of the study showed that the overall importance index computed using the FSE model is 3.65 (≈ 4) with an equivalent linguistic term of “very important”. The study also suggested that the top three HRM practices should be implemented conjointly as there is no significant difference among their weights.

The output of this research can provide important information regarding the HRM practices in the Australian construction industry. Thus, international developers or contractors who want to do construction business in Australia can implement the essential HRM practices so that the productivity of their construction projects will not be affected negatively.

Prefabricated housing is a construction method that largely reduces construction waste and promotes sustainable development. However, the adoption of this method of construction is inhibited by the lack of demand by consumers. The purpose of this research is to explore consumer education strategies aimed at overcoming prefabricated housing challenges in China.

The study conducted a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive search was conducted using databases such as Scopus and Web of Science, without any restriction on publication date. A total of 33 relevant articles were retrieved and analyzed using the content analysis method. The findings were then summarized and presented using the tabulation technique.

This study identified several barriers to prefabricated housing, including negative perception, limited public understanding, risk-averse culture and lack of green value. To foster market demand, relevant authorities should consider implementing effective educational mechanisms, such as comparative advertising, social media marketing, school programs and critical adult education programs. These strategies can effectively overcome the existing negative perception, alleviate concerns and create a positive environment for the growth of prefabricated housing in China.

This research provides practical and theoretical implications by identifying key challenges to prefabricated housing and suggesting targeted educational strategies to address them. It offers valuable guidance for policymakers, industry stakeholders and researchers, contributing to the advancement of sustainable housing practices.

Several factors influence the costs of buildings. Thus, identifying the cost significant factors can assist to improve the accuracy of project cost forecasts during the planning phase. This paper aims to identify the cost significant parameters and explore the potential for improving the accuracy of cost forecasts for buildings using machine learning techniques and large data sets.

The Australian State of Victoria Building Authority data sets, which comprise various parameters such as cost of the buildings, materials used, gross floor areas (GFA) and type of buildings, have been used. Five different machine learning regression models, such as decision tree, linear regression, random forest, gradient boosting and k-nearest neighbor were used.

The findings of the study showed that among the chosen models, linear regression provided the worst outcome (r² = 0.38) while decision tree (r² = 0.66) and gradient boosting (r² = 0.62) provided the best outcome. Among the analyzed features, the class of buildings explained about 34% of the variations, followed by GFA and walls, which both accounted for 26% of the variations.

The output of this research can provide important information regarding the factors that have major impacts on the costs of buildings in the Australian construction industry. The study revealed that the cost of buildings is highly influenced by their classes.

The building sector of the construction industry incorporates a precipitous and volatile nature with poor safety conditions being prevalent, owing to its inability to determine an appropriate trade-off between productivity and safety. This disproportionate trade-off produces production pressures, which contribute poorly to construction performance, by encouraging workers to prioritise their working productivity ahead of safety. The purpose of this study is to investigate the impacts of production pressures in the building sector and propose mitigation strategies accordingly.

A systematic review of literature was conducted, and secondary data were extracted from peer-reviewed journal papers. The data was then analysed to achieve the objectives of this study.

The main causes of production pressures are tight construction schedules, ineffective management and construction rework. Furthermore, the negative effects of production pressures are increased levels of stress in employees, reduced craftsmanship, encouraging accident-prone environments and decreasing employee’s safety behaviour. Effective mitigation strategies in relation to scheduling, leadership, communication and motivation were proposed. Finally, a causal loop diagram of production pressures in the building sector was developed.

This research will assist in creating a safer working environment within the building sector, by providing useful information regarding the severity of production pressures and suggesting mitigation strategies that can be implemented in the construction projects.

The complexities in strait-crossing cable-stayed bridge project are increasing the risks. This study aims to identify and analyze the significant and worth-considered construction risks of the first, biggest and longest spanned strait-crossing bridge project in Indonesia.

As many as 32 risk events were identified and determined as the risks that exist and can be represented in the Suramadu bridge project context. Data was collected through a design-based questionnaire disseminated to experts involved in the project as well as semi-formal interviews. Several quantitative methods were applied to analyze the significant risks, such as relative importance index, Spearman’s rank correlation test and Mann–Whitney U test.

The analyses reveal that “unexpected natural behavior” confirmed by both contractor and consultant parties is the most significant and crucial risk event. Another risk event found to be significant is the “delayed payment.” On the other hand, it is also found that several risks within the legal category are found to be less significant compared to other major risk events.

The results of the present research should be interpreted in the context of several limitations. Given these possible concerns regarding the generalizability of the findings, along with the relatively low rate of participants in the current research, additional studies are needed to provide a more complete picture of stakeholder perceptions who are involved directly in the construction environment as well as to identify more construction risks specifically in the large-scale bridge project.

This study has provided fundamental contributions to the body of knowledge and practical implication to promote and assist decision-makers toward developing a comprehensive risk assessment of a large-scale bridge project.

The analyses of outcomes and discussion, as well as the findings of this research, have shed light on the construction risks understanding, which contributes to delivering a theoretical framework for achieving large-scale bridge project success.

Building defects are becoming recurrent phenomena in most high-rise buildings. However, little research exists on the analysis of defects in high-rise buildings based on data from real-life projects. This study aims to develop dashboards and models for revealing the most common locations of defects, understanding associations among defects and predicting the rectification periods.

In total, 15,484 defect reports comprising qualitative and quantitative data were obtained from a company that provides consulting services for the construction industry in Victoria, Australia. Data mining methods were applied using a wide range of Python libraries including NumPy, Pandas, Natural Language Toolkit, SpaCy and Regular Expression, alongside association rule mining (ARM) and simulations.

Findings reveal that defects in multi-storey buildings often occur on lower levels, rather than on higher levels. Joinery defects were found to be the most recurrent problem on ground floors. The ARM outcomes show that the occurrence of one type of defect can be taken as an indication for the existence of other types of defects. For instance, in laundry, the chance of occurrence of plumbing and joinery defects, where paint defects are observed, is 88%. The stochastic model built for door defects showed that there is a 60% chance that defects on doors can be rectified within 60 days.

The dashboards provide original insight and novel ideas regarding the frequency of defects in various positions in multi-storey buildings. The stochastic models can provide a reliable point of reference for property managers, occupants and sub-contractors for taking measures to avoid reoccurring defects; so too, findings provide estimations of possible rectification periods for various types of defects.

Most residential building owners often report problems associated with the plumbing systems. If identified at the early stages, plumbing-related defects can be easily repaired. However, if unnoticed for a long period of time, they could lead to major damages and incur a significant cost to repair. Despite the problems, studies investigating plumbing anomalies and their root causes in residential buildings are limited. This study aims to explore plumbing defects and their potential causes, diagnosis methods and repair techniques in residential buildings.

This research used data collected through an extensive survey of both academic and grey literature. Through the content analysis, plumbing defects and the associated causes have been identified and presented in tabular format.

The study investigated the anomalies and causes in the residential plumbing system under five key sub-systems: water supply system; sanitary plumbing system; roof drainage system; heating, ventilation, air conditioning and gas system; and swimming pool. Accordingly, some of the identified plumbing defects include leakages, corrosion, water penetration, slow drainage and cracks. Damaged pipes, faulty equipment and installations are some of the common causes of the anomalies. Visual inspection, hydrostatic pressure test, thermography, high-tech pipe cameras, infrared cameras, leak noise correlators and leak loggers are techniques used for diagnosing anomalies. Reactive, preventive, predictive and reliability-centred maintenance strategies are identified to control or prevent anomalies.

The findings of this research can be used as a useful tool or guideline for contractors, plumbers, facilities managers and building surveyors to identify and rectify plumbing system-related defects in residential buildings.

Developing a reliable cost estimate at the early stage of construction projects is challenging due to inadequate project information. Most of the information during this stage is qualitative, posing additional challenges to achieving accurate cost estimates. Additionally, there is a lack of tools that use qualitative project information and forecast the budgets required for project completion. This research, therefore, aims to develop a model for setting project budgets (excluding land) during the pre-conceptual stage of residential buildings, where project information is mainly qualitative.

Due to the qualitative nature of project information at the pre-conception stage, a natural language processing model, DistilBERT (Distilled Bidirectional Encoder Representations from Transformers), was trained to predict the cost range of residential buildings at the pre-conception stage. The training and evaluation data included 63,899 building permit activity records (2021–2022) from the Victorian State Building Authority, Australia. The input data comprised the project description of each record, which included project location and basic material types (floor, frame, roofing, and external wall).

This research designed a novel tool for predicting the project budget based on preliminary project information. The model achieved 79% accuracy in classifying residential buildings into three cost_classes ($100,000-$300,000, $300,000-$500,000, $500,000-$1,200,000) and F1-scores of 0.85, 0.73, and 0.74, respectively. Additionally, the results show that the model learnt the contextual relationship between qualitative data like project location and cost.

The current model was developed using data from Victoria state in Australia; hence, it would not return relevant outcomes for other contexts. However, future studies can adopt the methods to develop similar models for their context.

This research is the first to leverage a deep learning model, DistilBERT, for cost estimation at the pre-conception stage using basic project information like location and material types. Therefore, the model would contribute to overcoming data limitations for cost estimation at the pre-conception stage. Residential building stakeholders, like clients, designers, and estimators, can use the model to forecast the project budget at the pre-conception stage to facilitate decision-making.

Freeways in Australia play a significant role in connecting distant communities, shifting freight and strengthening the country’s economy. To meet the growing needs of present and future generations, delivering a socially sustainable road infrastructure that creates generational benefits is essential. However, the existing literature reveals the lack of comprehensive indicators to assess the social sustainability performance of freeway projects. Therefore, this paper aims to identify a critical set of system-specific indicators to evaluate the life cycle social footprint of Australian freeways.

This study conducted 31 interview questionnaire surveys with actively engaged stakeholders involved in various freeway projects around Australia. The data collected was analysed using fuzzy set theory and other statistical approaches.

The study identified 42 critical indicators for assessing the social sustainability performance throughout the life cycle of freeways in the Australian context. For example, stakeholder involvement, reduction of casualty rate due to road accidents, fair remuneration to project workforce and improved accessibility to required services.

The context-specific opinions extracted from the industry experts and the comprehensive set of critical indicators identified would ensure that all the vital aspects of social sustainability are considered throughout the life cycle of Australian freeways in the future, assisting the decision-makers in enhancing the project’s social sustainability performance.

The linguistic explanations associated with the ratings given by the industry experts provide greater insight into the context of the life cycle social sustainability assessment of Australian freeways exclusively.