‘Big data analytics’ for construction firms insolvency prediction models

In a pioneering effort, this study is the first to develop a construction firms insolvency prediction model (CF-IPM) with Big Data Analytics (BDA); combine qualitative and quantitative variables; advanced artificial intelligence tools such as Random Forest and Bart Machine; and data of all sizes of construction firms (CF), ensuring wide applicabilityThe pragmatism paradigm was employed to allow the use of mixed methods. This was necessary to allow the views of the top management team (TMT) of failed and existing construction firms to be captured using a qualitative approach.TMT members of 13 existing and 14 failed CFs were interviewed. Interview result was used to create a questionnaire with over hundred qualitative variables. A total of 272 and 259 (531) usable questionnaires were returned for existing and failed CFs respectively. The data of the 531 questionnaires were oversample to get a total questionnaire sample of 1052 CFs. The original and matched sample financial data of the firms were downloaded. Using Cronbach’s alpha and factor analysis, qualitative variables were reduced to 13 (Q1 to Q13) while11 financial ratios (i.e. quantitative variables) (R1 and R11) reported by large and MSM CFs were identified for the sample CFs.The BDA system was set up with the Amazon Web Services Elastic Compute Cloud using five ‘Instances’ as Hadoop DataNodes and one as NameNode. The NameNode was configured as Spark Master. Eleven variable selection methods and three voting systems were used to select the final seven qualitative and seven quantitative variables, which were used to develop 13 BDA-CF-IPMs. The Decision Tree BDA-CF-IPM was the model of choice in this study because it had high accuracy, low Type I error and transparency. The most important variables (factors) affecting insolvency of construction firms according to the best model are returned on total assets; liquidity; solvency ratio; top management characteristics; strategic issues and external relations; finance and conflict related issues; industry contract/project knowledge

Art, Aesthetics and Innovations in The Built Environment: Beyond Theory into Practice

The way and manner in which construction development occurs within an environmentcharacterize the overall look of the neighbourhood and consequently the locality, city orcountry. Therefore, Art, Town and City planning, Architecture and Construction are the firstdeterminant factors that drive the innovation of any environment. The first three are the back ofthe house, while the latter is the front of the house. This article examines a series of academicwritings for how they posit on the training students in the built environment get and how wellthey are prepared to integrate into the evolving innovation dynamics of the constructionindustry. In addition, these literature pieces were studied for teaching methods, the mindsetinculcation, and the world's reality beyond classrooms. There is a reality in practice beyond thetheory and lectures of varsity rooms. While theory provides one with the required information tobuild on, practice affords one the real-time experience of managing thoughts into reality

Traffic-Related Air Pollutant (TRAP) Prediction using Big Data and Machine Learning

The negative impact of the Increasing air pollution on the global economy, quality of life of humans and health of animals and plants has been enormous. Several works of literature, reports and news around the world have highlighted the risk posed by the ever-in creasing air pollution and the threat to the lives of vulnerable groups such as children, the elderly, and people with respiratory and cardiovascular problems. The closest to home among all the air pollutants are the Traffic-Related Air Pollutants (TRAP), and they contribute the most to the risk posed to global health. This emphasises the urgency of the need for a highly accurate air pollution prediction model. Researchers have been able to achieve significant performance gain in predicting many of the pollutants except for the TRAP such as CO and NO which reported the worse prediction performance in many studies. CO and NO have been among the major pollutants of concern globally as they are linked to critical health hazards. Based on the established urgency of improving the accuracy of pollution prediction models, we collect recent data for six months and at high granularity in terms of time and location. The data is pre-processed and used to develop a Machine Learning (ML)based air pollution prediction model with high granularity and accuracy while focusing on traffic-related air pollutants CO and NO. Using the benchmarks r2and RMSE score, our ML models outperformed that of the studies reported in the literature for the prediction of TRAPs. This in part is due to the high data granularity we considered in terms of time and location

A Comparative Study on Machine Learning Algorithms for Predicting Construction Projects Delay

The perpetual occurrence of a global phenomenon – delay in construction industry despite considerable mitigation efforts remains a huge concern to its policy makers. Interestingly, this industry which produces massive amount of data from IoT sensors, building information modelling etc., on most of its projects daily is slow in taking the advantage of contemporary analysis method like machine learning (ML) which best explains factors that can affect a phenomenon like delay based on its predictive capabilities haven been widely adopted across other sectors. In this study therefore, a premise to compare the performance of machine learning algorithms for predicting delay of construction projects was proposed. To begin, a study of the existing body of knowledge on the factors that influence construction project delays was utilised to survey experts in order to obtain quantitative data. The generated dataset was used to train twenty-seven machine learning algorithms in order to develop predictive models. Results from the algorithm evaluation metrics: accuracy, balanced accuracy, Receiver Operating Characteristic Curve (ROC AUC), and f1-score indeed proved Perceptron model as the top performant model having achieved an accuracy, balanced accuracy, ROC AUC, and f1-score of 85%, 85%, 0.85 and 085 respectively higher than the rest of the models and unachieved in any previous study in predicting construction projects delay. Ultimately, this model can subsequently be integrated into construction information system to promote evidence based decision-making, thereby enabling constructive project risk management initiatives in the industry

Components reuse in the building sector – A systematic review

© The Author(s) 2020. The final, definitive version of this paper has been published in Rakhshan, K., Morel, J.-C., Alaka, H., & Charef, R. (2020). Components reuse in the building sector – A systematic review. Waste Management & Research, 38(4), 347–370 by Sage Publications Ltd. All rights reserved. It is available at: https://doi.org/10.1177/0734242X20910463.Widespread reuse of building components can promote the circularity of materials in the building sector. However, the reuse ofbuilding components is not yet a mainstream practise. Although there have been several studies on the factors affecting the reuse ofbuilding components, there is no single study that has tried to harmonize the circumstances affecting this intervention. Through asystematic literature review targeting peer-reviewed journal articles, this study intends to identify and stratify factors affecting thereuse of components of the superstructure of a building and eventually delineate correlations between these factors. Factors identifiedthroughout this study are classified into six major categories and 23 sub-categories. Then the inter-dependencies between the barriersare studied by developing the correlation indices between the sub-categories. Results indicate that addressing the economic, socialand regulatory barriers should be prioritized. Although the impact of barriers under perception, risk, compliance and market subcategoriesare very pronounced, the highest inter-dependency among the sub-categories is found between perception and risk. Itsuggests that the perception of the stakeholders about building components reuse is affected by the potential risks associated with thisintervention.Peer reviewedFinal Accepted Versio

Effects of Building Information Modeling on Construction Projects Delay: A Systematic Review

Time is a critical factor or primary success metric in measuring the progress of construction projects since they are normally time-bound. The construction industry, on the other hand, seldom completes projects on time due to its varied architecture - varying project styles, scopes, places, and sizes, as well as the participation of several stakeholders from different disciplines. Building Information Modeling (BIM) is expected to be a valuable tool in the construction industry, as it has the ability to mitigate construction project risks and complete projects successfully. As such, a systematic review on the effects of BIM on construction project delays become vital. Admittedly, systematic reviews provide a valuable opportunity for academics and practitioners to apply established expertise to further action, policy or study. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)guideline, this study thus aims to conduct a systematic review on the effects of BIM on construction projects delay. This research approach yielded a positive effect of BIM on delay across multiple regions of the world with different construction project types. This systemic review, as an evidence-based methodology, will be crucial for the Architecture Engineering Construction (AEC) industry in enforcing the adoption of BIM for current and future projects in the sector globally. It is recommended that a comprehensive systematic review be conducted on other pertinent issues common to the construction industry

An Application Of Machine Learning With Boruta Feature Selection To Improve NO2 Pollution Prediction

Projecting and monitoring NO2 pollutants' concentration is perhaps an efficient and effective technique to lower people's exposure, reducing the negative impact caused by this harmful atmospheric substance. Many studies have been proposed to predict NO2 Machine learning (ML) algorithm using a diverse set of data, making the efficiency of such a model dependent on the data/feature used. This research installed and used data from 14 Internet of thing (IoT) emission sensors, combined with weather data from the UK meteorology department and traffic data from the department for transport for the corresponding time and location where the pollution sensors exist. This paper select relevant features from the united data/feature set using Boruta Algorithm. Six out of the many features were identified as valuable features in the NO2 ML model development. The identified features are Ambient humidity, Ambient pressure, Ambient temperature, Days of the week, two-wheeled vehicles(counts), cars/taxis(counts). These six features were used to develop different ML models compared with the same ML model developed using all united data/features. For most ML models implemented, there was a performance improvement when developed using the features selected with Boruta Algorithm

Extraction of underlying factors causing construction projects delay in Nigeria

© 2021, Emerald Publishing Limited. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1108/JEDT-04-2021-0211Purpose This paper aims to establish the most underlying factors causing construction projects delay from the most applicable. Design/methodology/approach The paper conducted survey of experts using systematic review of vast body of literature which revealed 23 common factors affecting construction delay. Consequently, this study carried out reliability analysis, ranking using the significance index measurement of delay parameters (SIDP), correlation analysis and factor analysis. From the result of factor analysis, this study grouped a specific underlying factor into three of the six applicable factors that correlated strongly with construction project delay. Findings The paper finds all factors from the reliability test to be consistent. It suggests project quality control, project schedule/program of work, contractors’ financial difficulties, political influence, site conditions and price fluctuation to be the six most applicable factors for construction project delay, which are in the top 25% according to the SIDP score and at the same time are strongly associated with construction project delay. Research limitations/implications This paper is recommending that prospective research should use a qualitative and inductive approach to investigate whether any new, not previously identified, underlying factors that impact construction projects delay can be discovered as it followed an inductive research approach. Practical implications The paper includes implications for the policymakers in the construction industry in Nigeria to focus on measuring the key suppliers’ delivery performance as late delivery of materials by supplier can result in rescheduling of work activities and extra time or waiting time for construction workers as well as for the management team at site. Also, construction stakeholders in Nigeria are encouraged to leverage the amount of data produced from backlog of project schedules, as-built drawings and models, computer-aided designs (CAD), costs, invoices and employee details, among many others through the aid of state-of-the-art data driven technologies such as artificial intelligence or machine learning to make key business decisions that will help drive further profitability. Furthermore, this study suggests that these stakeholders use climatological data that can be obtained from weather observations to minimize impact of bad weather during construction. Originality/value This paper establishes the three underlying factors (late delivery of materials by supplier, poor decision-making and Inclement or bad weather) causing construction projects delay from the most applicable.Peer reviewe

A Comparative Study on Machine Learning Algorithms for Assessing Energy Efficiency of Buildings

© Springer Nature Switzerland AG 2021. This is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1007/978-3-030-93733-1_41An increase in energy demand in buildings continues to give rise to air pollution with a consequent impact on human health. To curb this trend, energy efficiency assessment plays a crucial role in helping to understand the energy in buildings and to recommend strategies to improve efficiency. Unfortunately, many existing approaches to assessing the energy efficiency of buildings are failing to do it accurately. Hence, the recommended energy efficiency strategies thereafter are failing to achieve the expected result. One approach in recent times uses data-driven predictive analytics techniques like machine learning (ML) algorithms to assess a building's energy efficiency towards improving its performance. However, as many ML algorithms exist, the selection of the right one is important for a successful assessment. Unfortunately, many of the existing works in this regard have simply adopted an ML algorithm without a justified rationale which may result in poor selection of the good performing ML algorithm. Therefore, in this study, a premise to compare the performance of ML algorithms for the assessment of energy efficiency of buildings was proposed. First, consolidated energy efficiency ratings of buildings from different data sources are used to develop predictive models using several ML algorithms. Thereafter, identification of best performing model was done by comparing evaluation metrics like RMSE, R-Squared, and Adjusted R-Squared. From the comparison, Extra Trees predictive model came top with RMSE, R-Squared, and Adjusted R-Squared of 2.79, 93%, and 93% respectively. This approach helps in the initial selection of suitable and better-performing ML algorithms