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Power plant projects are very complex and encounter serious cost overruns worldwide. Their cost overrun risks are not independent but interrelated in many cases, having structural relationships among each other. The purpose of this study is, therefore, to establish the complex structural relationships of risks involved.

In total, 76 published articles from the previous literature are reviewed using the content analysis method. Three risk networks in different phases of power plant projects are depicted based on literature review and case studies. The possible methods of solving these risk networks are also discussed.

The study finds critical cost overrun risks and develops risk networks for the procurement, civil and mechanical works of power plant projects. It identifies potential models to assess cost overrun risks based on the developed risk networks. The literature review also revealed some research gaps in the cost overrun risk management of power plants and similar infrastructure projects.

This study will assist project risk managers to understand the potential risks and their relationships to prevent and mitigate cost overruns for future power plant projects. It will also facilitate decision-makers developing a risk management framework and controlling projects’ cost overruns.

The study presents conceptual risk networks in different phases of power plant projects for comprehending the root causes of cost overruns. A comparative discussion of the relevant models available in the literature is presented, where their potential applications, limitations and further improvement areas are discussed to solve the developed risk networks for modeling cost overrun risks.

This research assesses the economic impact of biomass plant installations on Brazilian municipalities, focusing on (1) labor income, (2) sectoral labor income and (3) income inequality.

Municipal data from the Annual Social Information Report, the National Electric Energy Agency and the National Institute of Meteorology spanning 2002 to 2020 are utilized. The Synthetic Difference-in-Differences methodology is employed for empirical analysis, and robustness checks are conducted using the Doubly Robust Difference in Differences and the Double/Debiased Machine Learning methods.

The findings reveal that biomass plant installations lead to an average annual increase of approximately R$688.00 in formal workers' wages and reduce formal income inequality, with notable benefits observed for workers in the industry and agriculture sectors. The robustness tests support and validate the primary results, highlighting the positive implications of renewable energy integration on economic development in the studied municipalities.

This article represents a groundbreaking contribution to the existing literature as it pioneers the identification of the impact of biomass plant installation on formal employment income and local economic development in Brazil. To the best of our knowledge, this study is the first to uncover such effects. Moreover, the authors comprehensively examine sectoral implications and formal income inequality.

Industrial heritage is considered an essential part of cultural heritage in the world. This heritage suffers from continued marginalisation in the Arab world, particularly in Jordan, where many industrial heritage sites have not been protected or studied well due to the lack of a clear definition of cultural heritage. Most of these sites, built in the 20th century, are gradually disappearing or scheduled for demolition. This paper explores the de-industrialisation discourse and the loss of modern industrial heritage in the Arab world, especially in Jordan.

This research investigates the modern industrial heritage in Jordan as a case study in the Arab world. A comprehensive understanding of the industrial heritage has been obtained by adopting a case study approach and using a reconnaissance survey of potential industrial heritage sites in Jordan.

Seven categories were used in the analysis of the de-industrialisation phenomenon of heritage sites: ownership, location, design and types; structure, significance, deterioration and physical condition and conservation attempts and alterations. Three main approaches to industrial heritage were identified: demolition, occasional maintenance and rare examples of conservation and adaptive reuse.

This study sheds light on the ownership issue of industrial structures in Jordan and invites policymakers, relevant authorities, private organisations and the public to consider the challenges and impact of de-industrialisation of such sites.

This research raises awareness of the de-industrialisation discourse, and highlights the value of industrial architecture dating back to the modernity period, which was short-lived in Jordan. It also calls for serious consideration of these sites to support sustainable development in the Arab World.

In Bangladesh (BD), delays in infrastructure are common. Many previous studies have explored the causes of infrastructure delays. However, this study investigated the causes of delays by taking responses from the stakeholders who are responsible for planning, design, funding, approval and implementation. There are few studies that have related infrastructure project delays to heterogeneity in stakeholders’ perceptions.

A structural equation (SE) model is developed with 350 normally distributed data points to understand the heterogeneity in stakeholders’ perceptions regarding delays in infrastructure projects in BD. Additionally, the relative importance index (RII) approach is used to assess the responses, validating the SE model.

The study finds that among the three latent variables, “Project itself related delay” has more influence on delays in infrastructure projects. Among the observed variables under the “project itself related delay” latent variable, “DPP approval process” has the most significance. From the heterogeneity analysis, the study found differences in responses among the stakeholders from “the Engineering Department,” “the Planning Office” and “the Construction Firm/Industry.” An important class of stakeholders believes that their stage is not being delayed and that other stages require attention.

The data sample is 350. More data can improve the accuracy of the findings. Most of the respondents are civil engineers (74%) and represent the owner of the project. Sample data from more stakeholders’ will enhance the accuracy of the result.

This study addresses the requirements of Bangladeshi project stakeholders and how their interactions cause delays in projects. Furthermore, the opinions of other stakeholders are taken into consideration when determining the specific factors of individual stakeholders that are causing delays. Practically, the distance between stakeholders should be reduced. A project manager can play a role in this regard. Initiatives should be taken on how to complete the project quickly by eliminating the requirements discussed among the stakeholders and bureaucratic complications. Instead of placing blame on one another, stakeholders should take the initiative to figure out how to work together to finish the project on schedule. The Planning Commission’s approval of the Development Project Proposal (DPP) and Revised Development Project Proposal (RDPP) should be obtained as soon as possible by owner stakeholders. In order to avoid frequently changing the DPP, owners should also exercise greater caution when choosing contractors. Contractor stakeholders should use efficient and proper manpower and equipment so that unexpected delays are not created during the execution of work. Since the role of the contractor stakeholder is the most important among the three types of stakeholders, the contractor should raise awareness and urge the owners to get the RDPP approved quickly.

The findings from the study can help mitigate delays in infrastructure projects in BD, taking into account the perceptions of various stakeholders.

Megaprojects are financially large and complex in scope, which require engagement of stakeholders from various institution and communities. With increased concerns from stakeholders on sustainability related issues, from environmental to socio-economic perspective, managing megaprojects has become quite challenging. Hence, there should be proactive approaches in active engagement of stakeholders from the start of the projects to overcome such issues. Currently, there is a knowledge gap on stakeholder management strategies specific to megaprojects on managing sustainability issues.

This research has adopted review of secondary data from case studies to identify stakeholder management challenges and strategies in megaprojects around Sydney area. The documents related to three different projects were reviewed and data were collected through qualitative content analysis and coding with use of NVivo software for within case analysis and was followed by thematic approach for cross case analysis.

Every case study project's sustainability-related stakeholder issues are more similar than different to others. Differences are mostly in terms of intensity of impact of any issue in a particular project, and/or intensity of impact at certain phase of any project. Moreover, significant sectors and strategies have been identified for key focus to manage environment and socioeconomic issues. Whatever be the strategies, in case of megaprojects, they need to be mostly proactive.

This research has extended the knowledge on stakeholder management, in relation to managing sustainability-related issues of megaprojects during their design, construction and operation phases. This research offers useful strategies for project teams of megaprojects to develop a proactive stakeholder management plan, addressing unavoidable sustainability challenges.

Dam industry projects have significant economic, social and environmental impacts. However, very little has been carried out to improve their lifecycle performance. The purpose of this study is to identify success criteria applicable to different stages of such projects.

This study adopted a quantitative research design where the potential success criteria for dam engineering projects were evaluated. The applicable success criteria were determined for the four phases of project lifecycle by three rounds of Delphi technique with the participation of experts from dams industry in Australia.

The findings of this research suggest that project success is a multidimensional notion and varies over lifecycle of projects. This study on project success criteria shows that certain criteria can be applied to measure success in different phases over lifecycle of Australian dam industry projects.

The results of this research present the first exclusive quantitative assessment of success criteria for dams industry. The success criteria presented in this study enable project practitioners to measure success at various stages of dam industry projects. This can serve as a tool to put more management efforts into achieving success on those criteria.

Construction delay always causes massive damage to the advancing construction industries, which is no different in the case of Bangladeshi construction industries. This paper aims to investigate the major delay factors causing construction delays in public-funded, mixed and private-funded construction projects of Bangladesh. Also, it offers preventive suggestions from expert stakeholders to reduce the recurrence of delays.

At first, an extensive literature review was conducted to identify the thirty-seven major delay factors categorized under seven groups. A questionnaire was then developed for survey at ongoing construction projects at a different division of Bangladesh. Next, data from 110 respondents were collected, and the delay factors were ranked based on the Relative Importance Index (RII); lastly, probable solutions were suggested for top-ranked delay factors based on opinions from expert stakeholders in the construction sector of Bangladesh.

The overall RII ranking of the 37 delay factors showed “Construction mistakes and defective work,” “Contract modifications by the client” and “Adverse weather condition” as the top three factors causing the delay. For public-funded projects, “Construction mistakes and defective work” and “Slow decision making by a consultant” are the top delay factors. For mixed projects, “Slow decision making of the client” and “Construction mistakes and defective work ranked top, and for private-funded projects, “Financial problems and payment delay of the client” and “Adverse weather condition” ranked top. These nuances of ranking in individual project types ascertain that the causes of delay vary in terms of project features.

The outcome of this project will help identify the significant delay factors based on their severity of effectiveness associated with public-funded, mixed and private-funded projects in Bangladesh. The suggestions regarding preventing these delay factors obtained through the opinions of expert stakeholders can help reduce the effect of these delays in the context of Bangladesh and in countries where the similarity in construction environment prevails.

Previously, studies on construction delays in Bangladesh focused mainly on identifying the delays using qualitative analysis techniques. This study is based on a unique methodology of integrating quantitative research on delay factor identification and qualitative research on preventive measures following the opinions gathered from expert stakeholders in the construction sector.

Mega construction projects (MCPs), which play an important role in the economy, society and environment of a country, have developed rapidly in recent years. However, due to frequent social conflicts caused by the negative social impact of MCPs, social risk control has become a major challenge. Exploring the relationship between social risk factors and social risk from the perspective of risk evolution and identifying key factors contribute to social risk control; but few studies have paid enough attention to this. Therefore, this study aims to systematically analyze the impact of social risk factors on social risk based on a social risk evolution path.

This study proposed a social risk evolution path for MCPs explaining how social risk occurs and develops with the impact of social risk factors. To further analyze the impact quantitatively, a social risk analysis model combining structural equation model (SEM) with Bayesian network (BN) was developed. SEM was used to verify the relationship in the social risk evolution path. BN was applied to identify key social risk factors and predict the probabilities of social risk, quantitatively. The feasibility of the proposed model was verified by the case of water conservancy projects.

The results show that negative impact on residents’ living standards, public opinion advantage and emergency management ability were key social risk factors through sensitivity analysis. Then, scenario analysis simulated the risk probability results with the impact of different states of these key factors to obtain management strategies.

This study creatively proposes a social risk evolution path describing the dynamic interaction of the social risk and first applies the hybrid SEM–BN method in the social risk analysis for MCPs to explore effective risk control strategies. This study can facilitate the understanding of social risk from the perspective of risk evolution and provide decision-making support for the government coping with social risk in the implementation of MCPs.

This study aimed to identify a specific taxonomy of entry modes for international construction contractors and to develop a decision-making mechanism based on case-based reasoning (CBR) to facilitate the selection of the most suitable entry modes.

According to the experience orientation of the construction industry, a CBR entry mode decision model was established, and based on successful historical cases, a two-step refinement process was carried out to identify similar situations. Then the validity of the model is proved by case analysis.

This study identified an entry mode taxonomy for international construction contractors (ICCs) and explored their decision-making mechanisms. First, a two-dimension model of entry mode for ICCs was constructed from ownership and value chain dimensions; seven common ICC entry modes were identified and ranked according to market commitment. Secondly, this study reveals the impact mechanism of the ICC entry mode from two aspects: the external environment and enterprise characteristics. Accordingly, an entry mode decision model is established.

Firstly, sorting out the categories of entry mode in the construction field, which provide an entry mode list for ICCs to select. Secondly, revealing the impact mechanism of ICC entry mode, which proposes a systematic decision-making system for the selection of ICC entry mode. Thirdly, constructing a CBR entry mode decision-making model from an empirical perspective, which offers tool support and reduces transaction costs in the decision-making process.

The study on entry modes for ICCs is still in the preliminary exploratory stage. The authors investigate the entry mode categories and decision-making mechanisms for ICCs based on Uppsala internationalization process theory. It widens the applied scope of Uppsala and promotes cross-disciplinary integration. In addition, the authors creatively propose a two-stage retrieval mechanism in the CBR model, which considers the order of decision variables. It refines the influence path of the decision variables on ICCs' entry mode.

Existing labor estimation models typically consider only certain construction project types or specific influencing factors. These models are focused on quantifying the total labor hours required, while the utilization rate of the labor during the project is not usually accounted for. This study aims to develop a novel machine learning model to predict the time series of labor resource utilization rate at the work package level.

More than 250 construction work packages collected over a two-year period are used to identify the main contributing factors affecting labor resource requirements. Also, a novel machine learning algorithm – Recurrent Neural Network (RNN) – is adopted to develop a forecasting model that can predict the utilization of labor resources over time.

This paper presents a robust machine learning approach for predicting labor resources’ utilization rates in construction projects based on the identified contributing factors. The machine learning approach is found to result in a reliable time series forecasting model that uses the RNN algorithm. The proposed model indicates the capability of machine learning algorithms in facilitating the traditional challenges in construction industry.

The findings point to the suitability of state-of-the-art machine learning techniques for developing predictive models to forecast the utilization rate of labor resources in construction projects, as well as for supporting project managers by providing forecasting tool for labor estimations at the work package level before detailed activity schedules have been generated. Accordingly, the proposed approach facilitates resource allocation and enables prioritization of available resources to enhance the overall performance of projects.