Muhammad Bilal

Big Data in the construction industry

Existing works for Big Data Analytics/Engineering in the construction industry are discussed.It is highlighted that the adoption of Big Data is still at nascent stageOpportunities to employ Big Data technologies in construction sub-domains are highlighted.Future works for Big Data technologies are presented.Pitfalls of Big Data technologies in the construction industry are also pointed out. 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.

Analysis of critical features and evaluation of BIM software: towards a plug-in for construction waste minimization using big data

The overall aim of this study is to investigate the potential of Building Information Modelling (BIM) for construction waste minimization. We evaluated the leading BIM design software products and concluded that none of them currently support construction waste minimization. This motivates the development of a plug-in for predicting and minimizing construction waste. After a rigorous literature review and conducting four focused group interviews (FGIs), 12 imperative BIM factors were identified that should be considered for predicting and designing out construction waste. These factors were categorized into four layers, namely the BIM core features layer, the BIM auxiliary features layer, the waste management criteria layer, and the application layer. Further, a process to carry out BIM-enabled building waste analysis (BWA) is proposed. We have also investigated the usage of big data technologies in the context of waste minimization. We highlight that big data technologies are inherently suitable for BIM due to their support of storing and processing large datasets. In particular, the use of graph-based representation, analysis, and visualization can be employed for advancing the state of the art in BIM technology for construction waste minimization.

Critical management practices influencing on-site waste minimization in construction projects

As a result of increasing recognition of effective site management as the strategic approach for achieving the required performance in construction projects, this study seeks to identify the key site management practices that are requisite for construction waste minimization. A mixed methods approach, involving field study and survey research were used as means of data collection. After confirmation of construct validity and reliability of scale, data analysis was carried out through a combination of Kruskal-Wallis test, descriptive statistics and exploratory factor analysis. The study suggests that site management functions could significantly reduce waste generation through strict adherence to project drawings, and by ensuring fewer or no design changes during construction process. Provision of waste skips for specific materials and maximisation of on-site reuse of materials are also found to be among the key factors for engendering waste minimization. The result of factor analysis suggests four factors underlying on-site waste management practices with 96.093% of total variance. These measures include contractual provisions for waste minimization, waste segregation, maximisation of materials reuse and effective logistic management. Strategies through which each of the underlying measures could be achieved are further discussed in the paper. Findings of this study would assist construction site managers and other site operatives in reducing waste generated by construction activities.

SDNs, Clouds, and Big Data: New Opportunities

We are at the cusp of a technological revolution driven mainly by advances in hardware technology, network architectural support, and the ability to process big data. The hardware industry, driven by Moores law, continues to provide steadily increasing computing capability with diminishing costs. With the support of hardware advances, platforms for distributed storage and processing of big data, such as Apache Hadoop, provide the ability to scalably and reliably process massive amounts of data using a cluster of commodity servers. In parallel, a revolution is ongoing in the networking world. The emergence of the software defined networking (SDN) architecture promises to rectify the ossified architecture of the Internet allowing network managers to flexibly program the network. In this paper, we argue that SDN and big data, two technological trends that promise to revolutionize all aspects of modern life, can leverage the freedom afforded by each other to jointly increase their value proposition. In particular, SDN can utilize the large amounts of operational data to optimize the network behavior, while big data platforms can benefit from the flexible architectural support provided by SDN. In this paper, we will provide a brief self-contained exposition on opportunities for SDN and big data to synergize and jointly optimize. We will also point out open research issues and will identify future directions of work.

Usage control model specification in XACML policy language

Usage control model (UCON) is one of the emerging and comprehensive attribute based access control model that has the ability of monitoring the continuous updates in a system making it better than the other models of access control. UCON is suitable for the distributed environment of grid and cloud computing platforms however the proper formulation of this model does not exist in literature in any policy specification standard. It is for this reason that UCON is not widely adopted as an access control model by industry, though research community is now paying attention to make standard policy specification for this model. In this paper we are suggesting the interpretation of UCON model in extensible access control markup language (XACML) which is an OASIS standard of access control policies. We also highlight UCON model features by explaining its core processes and characteristics with respect to the case study of financial application.

Systematic Review of Bankruptcy Prediction Models: Towards A Framework for Tool Selection

The bankruptcy prediction research domain continues to evolve with many new different predictive models developed using various tools. Yet many of the tools are used with the wrong data conditions or for the wrong situation. Using the Web of Science, Business Source Complete and Engineering Village databases, a systematic review of 49 journal articles published between 2010 and 2015 was carried out. This review shows how eight popular and promising tools perform based on 13 key criteria within the bankruptcy prediction models research area. These tools include two statistical tools: multiple discriminant analysis and Logistic regression; and six artificial intelligence tools: artificial neural network, support vector machines, rough sets, case based reasoning, decision tree and genetic algorithm. The 13 criteria identified include accuracy, result transparency, fully deterministic output, data size capability, data dispersion, variable selection method required, variable types applicable, and more. Overall, it was found that no single tool is predominantly better than other tools in relation to the 13 identified criteria. A tabular and a diagrammatic framework are provided as guidelines for the selection of tools that best fit different situations. It is concluded that an overall better performance model can only be found by informed integration of tools to form a hybrid model. This paper contributes towards a thorough understanding of the features of the tools used to develop bankruptcy prediction models and their related shortcomings.

Evaluation criteria for construction waste management tools: towards a holistic BIM framework

AbstractThis study identifies evaluation criteria with the goal of appraising the performance of existing construction waste management tools and employing the results in the development of a holistic building information modelling (BIM) framework for construction waste management. Based on the literature, this paper identifies 32 construction waste management tools in five categories: (a) waste management plan templates and guides, (b) waste data collection and audit tools (c) waste quantification models, (d) waste prediction tools, and (e) geographic information system (GIS)-enabled waste tools. After reviewing these tools and conducting four focus-group interviews (FGIs), the findings revealed six categories of evaluation criteria (a) waste prediction; (b) waste data; (c) commercial and procurement; (d) BIM; (e) design; and (f) technological. The performance of the tools is assessed using the evaluation criteria and the result reveals that the existing tools are not robust enough to tackle construction...

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

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 Cronbachs 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 Porters five competitive forces and Mintzbergs 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.

Competency-based measures for designing out construction waste: task and contextual attributes

– Competency-based measure is increasingly evident as an effective approach to tailoring training and development for organisational change and development. With design stage widely reckoned as being decisive for construction waste minimisation, the purpose of this paper is to identify designers’ competencies for designing out waste. , – Due to paucity of research into competency for construction waste mitigation, this study corroborates verbal protocol analyses (VPA) with phenomenological research. , – Combining findings from the two methodological approaches, competencies for designing out waste are grouped into five categories, three of which are largely task related and two being contextual competencies. The study suggests that design task proficiency, low waste design skills and construction-related knowledge are indispensable task competencies, while behavioural competence and inter-professional collaborative abilities are requisite contextual competencies for designing out waste. In concurrence with task-contextual theory of job performance, personality variables and cognitive abilities are found to influence one another. This suggests that both task and contextual competencies are not only important, they are less mutually exclusive with respect to designing out waste. , – This study implies that apart from commitment and dedication of designers to waste minimisation, design and firm practices are expected to be adapted to the industry’s standard. , – Basis for training needs of design professionals as well as redeployment criterion are further elaborated in the paper. By enhancing competencies identified in this study, construction waste would not only be significantly designed out, adequate cost saving could be made as a result of waste reduction.

Methodological approach of construction business failure prediction studies: a review

Performance of bankruptcy prediction models (BPM), which partly depends on the methodological approach used to develop it, has virtually stagnated over the years. The methodological positions of BPM studies were thus investigated. Systematic review was used to search and retrieve 70 journal articles and doctoral theses. Their “general methods” and “philosophical underpinnings” were investigated using summary of findings tables and meta-analysis. “General methods” results showed positive trends in terms of techniques being used, error cost consideration and model validation, with some use of skewed data being the main drawback. For “philosophical underpinnings”, positivism paradigm was discovered to be at the core of BPM studies. This is deemed inadequate because of the need to consider industries’ dynamism, financial variables flaws and social factors which actually lead to the financial status of firms. The pragmatism paradigm using mixed method is proposed. A research design framework for executing the proposed methodology is presented. This will help BPM developers go through more rigorous and robust methodology to deliver better and more valid models. Limitations of study include not reviewing studies not reported in English language and impact of different countries’ accounting practices on ratios. Limited availability of theses’ database resulted in reviewing only four theses.