Reverse supply chain conceptual model for construction and demolition waste.

Construction and demolition waste (CDW) substantially contributes to environmental degradation because of its intrinsic characteristics of fast and high generation volume, low recycling rate, and low revenue margins. A systemic problem is that recycling facilities are not usually a part of a reverse supply chain (RSC) specific for CDW. This makes the recovery process costs prohibitive, especially where companies are unable to receive and process large volumes of waste continuously. This paper presents a systematic analysis of the extant literature and utilizes the results accrued to develop a conceptual RSC model for CDW. In so doing, the research seeks to provide clarity on this phenomenon, while simultaneously stimulating wider academic discourse and further research endeavours. A mixed philosophies epistemological design was adopted using both interpretavism and constructivism to undertake a qualitative systematic analysis of the literature. A process diagram was produced to represent the conceptual model (CM) and thematically group the nodes into three key swim lanes that delineate the boundaries between distribution, manufacturing, and sourcing and warehousing processes. Within each swim lane, stakeholders were incorporated as key actors. A further layer of nuanced complexity was added to illustrate the key actors involved in the process, government strategies, and activity flow paths. This novel CM offers both practical and theoretical contributions to existing knowledge and signposts a future research direction. Such work will demystify reverse logistics for managing CDW, and assist government policy-makers to develop informed policies that reduce the negative environmental impact of construction activities.

Developing an Ensemble Predictive Safety Risk Assessment Model: Case of Malaysian Construction Projects.

Occupational Health and Safety (OHS)-related injuries are vexing problems for construction projects in developing countries, mostly due to poor managerial-, governmental-, and technical safety-related issues. Though some studies have been conducted on OHS-associated issues in developing countries, research on this topic remains scarce. A review of the literature shows that presenting a predictive assessment framework through machine learning techniques can add much to the field. As for Malaysia, despite the ongoing growth of the construction sector, there has not been any study focused on OHS assessment of workers involved in construction activities. To fill these gaps, an Ensemble Predictive Safety Risk Assessment Model (EPSRAM) is developed in this paper as an effective tool to assess the OHS risks related to workers on construction sites. The developed EPSRAM is based on the integration of neural networks with fuzzy inference systems. To show the effectiveness of the EPSRAM developed, it is applied to several Malaysian construction case projects. This paper contributes to the field in several ways, through: (1) identifying major potential safety risks, (2) determining crucial factors that affect the safety assessment for construction workers, (3) predicting the magnitude of identified safety risks accurately, and (4) predicting the evaluation strategies applicable to the identified risks. It is demonstrated how EPSRAM can provide safety professionals and inspectors concerned with well-being of workers with valuable information, leading to improving the working environment of construction crew members.

Distinguishing Characteristics of Corruption Risks in Iranian Construction Projects: A Weighted Correlation Network Analysis.

The construction industry consistently ranks amongst the highest contributors to global gross domestic product, as well as, amongst the most corrupt. Corruption therefore inflicts significant risk on construction activities, and overall economic development. These facts are widely known, but the various sources and nature of corruption risks endemic to the Iranian construction industry, along with the degree to which such risks manifest, and the strength of their impact, remain undescribed. To address the gap, a mixed methods approach is used; with a questionnaire, 103 responses were received, and these were followed up with semi-structured interviews. Results were processed using social network analysis. Four major corruption risks were identified: (1) procedural violations in awarding contracts, (2) misuse of contractual arrangements, (3) neglect of project management principles, and, (4) irrational decision making. While corruption risks in Iran align with those found in other countries, with funds being misappropriated for financial gain, Iran also shows a strong inclination to champion projects that serve the government's political agenda. Root cause identification of corruption risks, namely, the noticeable impact of authoritarianism on project selection in Iran, over criterion of economic benefit or social good, is a significant outcome of this study.

Demographic Variables of Corruption in the Chinese Construction Industry: Association Rule Analysis of Conviction Records.

Corruption in the construction industry is a serious problem in China. As such, fighting this corruption has become a priority target of the Chinese government, with the main effort being to discover and prosecute its perpetrators. This study profiles the demographic characteristics of major incidences of corruption in construction. It draws on the database of the 83 complete recorded cases of construction related corruption held by the Chinese National Bureau of Corruption Prevention. Categorical variables were drawn from the database, and 'association rule mining analysis' was used to identify associations between variables as a means of profiling perpetrators. Such profiling may be used as predictors of future incidences of corruption, and consequently to inform policy makers in their fight against corruption. The results signal corruption within the Chinese construction industry to be correlated with age, with incidences rising as managers' approach retirement age. Moreover, a majority of perpetrators operate within government agencies, are department deputies in direct contact with projects, and extort the greatest amounts per case from second tier cities. The relatively lengthy average 6.4-year period before cases come to public attention corroborates the view that current efforts at fighting corruption remain inadequate.

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.

Reverse logistics in the construction industry.

Reverse logistics in construction refers to the movement of products and materials from salvaged buildings to a new construction site. While there is a plethora of studies looking at various aspects of the reverse logistics chain, there is no systematic review of literature on this important subject as applied to the construction industry. Therefore, the objective of this study is to integrate the fragmented body of knowledge on reverse logistics in construction, with the aim of promoting the concept among industry stakeholders and the wider construction community. Through a qualitative meta-analysis, the study synthesises the findings of previous studies and presents some actions needed by industry stakeholders to promote this concept within the real-life context. First, the trend of research and terminology related with reverse logistics is introduced. Second, it unearths the main advantages and barriers of reverse logistics in construction while providing some suggestions to harness the advantages and mitigate these barriers. Finally, it provides a future research direction based on the review.