Predicting Completion Risk in PPP Projects using Big Data Analytics

Accurate prediction of potential delays in public private partnerships (PPP) projects could provide valuable information relevant for planning and mitigating completion risk in future PPP projects. However, existing techniques for evaluating completion risk remain incapable of identifying hidden patterns in risk behavior within large samples of projects, which are increasingly relevant for accurate prediction. To effectively tackle this problem in PPP projects, this study proposes a Big Data Analytics predictive modeling technique for completion risk prediction. With data from 4294 PPP project samples delivered across Europe between 1992 and 2015, a series of predictive models have been devised and evaluated using linear regression, regression trees, random forest, support vector machine, and deep neural network for completion risk prediction. Results and findings from this study reveal that random forest is an effective technique for predicting delays in PPP projects, with lower average test predicting error than other legacy regression techniques. Research issues relating to model selection, training, and validation are also presented in the study

Insolvency of small civil engineering firms: Critical strategic factors

© 2016 American Society of Civil Engineers. Construction industry insolvency studies have failed to stem the industry's high insolvency tide because many focus on big civil engineering firms (CEF) when over 90% of firms in the industry are small or micro (S&M). This study thus set out to uncover insolvency criteria of S&M CEFs and the underlying factors using mixed methods. Using convenience sampling, the storytelling method was used to execute interviews of 16 respondents from insolvent firms. Narrative and thematic analysis were used to extract 17 criteria under 2 groups. Criteria were used to formulate a questionnaire, of which 81 completed copies were received and analyzed using Cronbach's alpha coefficient and relevance index score for reliability and ranking, respectively. The five most relevant criteria were economic recession, immigration, too many new firms springing up, collecting receivables, and burden of sustainable construction. The four underlying factors established through factor analysis were market forces, competence-based management, operations efficiency and other management issues, and information management. The factors were in line with Mintzberg's and Porter's strategy theories. The results demonstrate that insolvency factors affecting big and small CEF can be quite different and, sometimes, even opposite. This research will provide a unique resource on the factors that should make potential owners of S&M CEF cautious. The criteria are potential variables for insolvency prediction models for S&M CEFs

Designing out construction waste using BIM technology:Stakeholders’ expectations for industry deployment

The need to use Building Information Modelling (BIM) for Construction and Demolition Waste (CDW) minimisation is well documented but most of the existing CDW management tools still lack BIM functionality. This study therefore assesses the expectations of stakeholders on how BIM could be employed for CDW management. After a review of extant literature to assess the limitations of existing CDW management tools, qualitative Focus Group Interviews (FGIs) were conducted with professionals who are familiar with the use of BIM to understand their expectations on the use of BIM for CDW management. The 22 factors identified from the qualitative data analyses were then developed into a questionnaire survey. The exploratory factor analysis of the responses reveals five major groups of BIM expectations for CDW management, which are: (i) BIM-based collaboration for waste management, (ii) waste-driven design process and solutions, (iii) waste analysis throughout building lifecycle, (iv) innovative technologies for waste intelligence and analytics, and (v) improved documentation for waste management. Considering these groups of factors is key to meeting the needs of the stakeholders regarding the use of BIM for CDW management. These groups of factors are important considerations for the implementation and acceptance of BIM-based tools and practices for CDW management within the construction industry.<br/

Reducing waste to landfill: A need for cultural change in the UK construction industry

© 2015 Elsevier Ltd. All rights reserved. Owing to its contribution of largest portion of landfill wastes and consumption of about half of mineral resources excavated from nature, construction industry has been pressed to improve its sustainability. Despite an adoption of several waste management strategies, and introduction of various legislative measures, reducing waste generated by the industry remains challenging. In order to understand cultural factors contributing to waste intensiveness of the industry, as well as those preventing effectiveness of existing waste management strategies, this study examines cultural profile of construction industry. Drawing on four focus group discussions with industry experts, the study employs phenomenological approach to explore waste inducing cultural factors. Combining findings from phenomenological research with extant literatures, the study suggests that in order to reduce waste intensiveness of the construction industry, five waste inducing cultural factors need to be changed. These include (i) "make-do" understanding that usually result in "make-do waste" (ii) non-collaborative culture, which results in reworks and other forms of wasteful activities (iii) blame culture, which encourages shifting of waste preventive responsibilities between designers and contractors, (iv) culture of waste behaviour, which encourages belief in waste inevitability, and (v) conservatism, which hinders diffusion of innovation across the industry. Changing these sets of cultural and behavioural activities is not only important for engendering waste management practices; they are requisite for effectiveness of existing strategies. Improvement in the identified areas is also required for overall improvement and general resource efficiency of the construction industry. Thus, this paper advocates cultural shift as a means of reducing waste landfilled by the construction industry, thereby enhancing sustainability and profitability of the industry

Critical factors for insolvency prediction: Towards a theoretical model for the construction industry

© 2016 Informa UK Limited, trading as Taylor & Francis Group. Many construction industry insolvency prediction model (CI-IPM) studies have arbitrarily employed or simply adopted from previous studies different insolvency factors, without justification, leading to poorly performing CI-IPMs. This is due to the absence of a framework for selection of relevant factors. To identify the most important insolvency factors for a high-performance CI-IPM, this study used three approaches. Firstly, systematic review was used to identify all existing factors. Secondly, frequency of factor use and accuracy of models in the reviewed studies were analysed to establish the important factors. Finally, using a questionnaire survey of CI professionals, the importance levels of factors were validated using the Cronbach's alpha reliability coefficient and significant index ranking. The findings show that the important quantitative factors are profitability, liquidity, leverage, management efficiency and cash flow. While important qualitative factors are management/owner characteristics, internal strategy, management decision making, macroeconomic firm characteristics and sustainability. These factors, which align with existing insolvency-related theories, including Porter's five competitive forces and Mintzberg's 5Ps (plan, ploy, pattern, position and perspective) of strategy, were used to develop a theoretical framework. This study contributes to the debate on the need to amalgamate qualitative and quantitative factors to develop a valid CI-IPM

Big Data in the construction industry: A review of present status, opportunities, and future trends

© 2016 Elsevier Ltd The ability to process large amounts of data and to extract useful insights from data has revolutionised society. This phenomenon—dubbed as Big Data—has applications for a wide assortment of industries, including the construction industry. The construction industry already deals with large volumes of heterogeneous data; which is expected to increase exponentially as technologies such as sensor networks and the Internet of Things are commoditised. In this paper, we present a detailed survey of the literature, investigating the application of Big Data techniques in the construction industry. We reviewed related works published in the databases of American Association of Civil Engineers (ASCE), Institute of Electrical and Electronics Engineers (IEEE), Association of Computing Machinery (ACM), and Elsevier Science Direct Digital Library. While the application of data analytics in the construction industry is not new, the adoption of Big Data technologies in this industry remains at a nascent stage and lags the broad uptake of these technologies in other fields. To the best of our knowledge, there is currently no comprehensive survey of Big Data techniques in the context of the construction industry. This paper fills the void and presents a wide-ranging interdisciplinary review of literature of fields such as statistics, data mining and warehousing, machine learning, and Big Data Analytics in the context of the construction industry. We discuss the current state of adoption of Big Data in the construction industry and discuss the future potential of such technologies across the multiple domain-specific sub-areas of the construction industry. We also propose open issues and directions for future work along with potential pitfalls associated with Big Data adoption in the industry

Digital leadership enactment in the construction industry: barriers undermining effective transformation

Purpose In the past decade, transforming key processes and activities towards a more digital nature has been the focus of most industries due to the associated advantages. Despite that, organisations in the construction sector are lagging the list of early adopters. The slow rate of a fundamental digital transformation is linked to the challenges facing an effective leadership. The purpose of this paper is, therefore, to shed light on the barriers to digital leadership enactment in the construction industry. Limited research has empirically analysed and discussed these barriers to explain the low transformation rate in the existing body of knowledge. Design/methodology/approach This paper empirically investigates the perspectives of construction industry professionals acquiring various roles in the industry. Overall, the study comprises the views of 38 participants, adopting a qualitative methodological approach to explore relative barriers and explain the slow digital transformation rate. Findings Findings are grouped into five themes: leadership characteristics, management and organisational issues, resource constraints, technological issues, and risk perceptions. The findings are helpful to business leaders, researchers, trainers, and educators to develop measures to encourage leaders in the industry to be at the forefront of digital transformation in their organisations. Originality/value Literature, however, is discreet in reflecting the challenges and barriers facing today’s leadership in facilitating digital transformation among construction stakeholders. This paper provides insights into the variables that may be undermining wider digital adoption across the construction sector’s organisations

Insolvency of Small Civil Engineering Firms: An Examination of Critical Strategic Factors

Construction industry insolvency studies have failed to stem the industry’s high insolvency tide because many focus on big civil engineering firms (CEF) when over 90% firms in the industry are small or micro (S&M). This study thus set out to uncover insolvency criteria of S&M CEFs and the underlying factors using mixed methods. Using convenience sampling, storytelling method was used to execute interviews of 16 respondents from insolvent firms. Narrative and thematic analysis were used to extract 17 criteria under 2 groups. Criteria were used to formulate questionnaire of which 81 completed copies were received and analysed using Cronbach’s alpha coefficient and relevance index score for reliability and ranking respectively. The five most relevant criteria are: economic recession, immigration, too many new firms springing up, collecting receivables and burden of sustainable construction. The 4 underlying factors established through factor analysis are: market forces, competence-based management, operations efficiency and other management issues and information management. The factors were in line with Mintzberg’s and Porters’ strategy theories. Results demonstrate that insolvency factors affecting big and small CEF can be quite different and sometimes, even opposite. This research will provide a unique resource on the ‘beware’ factors for potential owners of S&M CEF. The criteria are potential variables for insolvency prediction models for S&M CEFs

Policy Imperatives for Diverting Construction Waste from Landfill: Experts’ recommendations for UK policy expansion

Legislation and fiscal policies have remained the key drivers of construction waste minimization. It has often been suggested that reducing waste to the landfill does not only require improvement on existing waste management policies and fiscal framework; there is a need for adequate inputs from the construction professionals. As a means of engendering effective waste management policies, this study explores industry practitioners’ viewpoints on effective policies for minimising waste landfilled by the UK construction industry. Using exploratory sequential mixed method approach, qualitative and quantitative methods were used. In the first phase of the study, data was collected through focus group discussions with 24 experts from the UK construction industry. Findings from the qualitative study served as an input into a questionnaire, which was used to elicit a wider opinion from 63 experts at the quantitative stage of the study. The study suggests that for waste management legislation and policies to effectively drive construction waste minimization, six key measures are important. These include (i) provision of tax breaks and incentives to good waste performers and waste management businesses; (ii) increased targeting of design stages in policies; (iii) Extension of sustainable design appraisal systems by allocating more points to proven waste performance measures; (iv) increased stringency of legislative measures by requiring use of proven waste efficient design, procurement and construction methods; (v) increased stringency of fiscal policies by increasing penalties for poor waste performance; and (vi) corroboration of policy requirements with enablers and facilitators. The strategies through which each of the legislative and fiscal measures could be tailored and enhanced are discussed in the paper. By embracing both stringent and palliative policy measures suggested in the study, substantial construction waste could be diverted from landfill