Muizz O Sanni-Anibire

Purpose Although there is a boom in the construction industry in the Kingdom of Saudi Arabia (KSA), it is yet to fully adopt building information modeling (BIM), which has received a lot of attention in the US, UK and Australian construction industries. Thus, the purpose of this paper is to provide the current state of the art in BIM implementation in Saudi Arabia, as well as perceived benefits and barriers through a case study. Design/methodology/approach A broad overview of BIM, the construction industry in KSA and the research and implementation of BIM in KSA was presented in this study. The research further established the perceived benefits and barriers of BIM implementation through a case study of a local AEC firm. A questionnaire survey was used to obtain lessons learned from the BIM team of the pilot project and was further analyzed using the RII approach. Findings The study’s findings include the lack of policy initiatives in KSA to enforce BIM in the construction industry,...

Purpose The purpose of this paper is to present the post-occupancy evaluation (POE) of academic and research laboratory facilities. This is based on the premise that the performance of such facilities is directly connected to the productivity, health and wellbeing of its users. Design/methodology/approach The paper was carried out in two phases, first, the performance requirements for academic and laboratory facilities were identified through the extant literature. Furthermore, a questionnaire survey was developed to carry out a POE of existing academic and research laboratory facilities in a university campus in Saudi Arabia. The questionnaire was designed using a Likert scale of one to five. Finally, the satisfaction index was computed, and rates of satisfaction with the various performance requirements were determined. Findings The paper resulted into 74 performance requirements grouped into three categories. The respondents showed general satisfaction with most of the performanc...

The construction industry is one of the most dangerous industries worldwide due to deadly fatalities and accidents recorded yearly. Though many countries have established and implemented safety programs, the situation does not seem to have been mitigated. This study aims at developing a risk assessment approach that can be used to enhance the safety performance of construction projects. The study has employed pair-wise comparisons and weighting-by-ranking surveys to establish risk scores and weights for the various construction accidents, and their potential causes. Data has been collected from safety professionals in 15 large construction sites across the Eastern Province of Saudi Arabia. The study revealed that the type of accident with the highest risk score is "falling objects", while the most significant cause is excessive winds on the project site. The developed approach was applied on an ongoing car park construction project. Results showed that slips, trips and falls had the best safety performance. Furthermore, based on six sigma evaluation, the average project safety performance was at 2.33-sigma which implies that 228,739 accidents may occur in every million opportunities. The paper also provided recommendations to improve the safety performance of the case study.

Purpose The purpose of this paper is to present the development and implementation of a qualitative, code-compliance framework for property managers of student housing facilities. Design/methodology/approach The paper identified the fire safety code requirements for student housing facilities and arranged these requirements in the form of a checklist, which was further validated by professional experts. Additionally, the paper presented an IDEF0 (Integrated Definition for Function Modeling) framework model that illustrates a stepwise process for the deployment of the checklist. A case study was conducted on three similar student housing facilities in a university campus to demonstrate the application of the framework. Furthermore, the findings from the case study were reported along with recommendations to improve the degree of compliance with the requirements of fire safety codes. Findings The developed framework was validated by professional experts and through a case study. Fire ...

Purpose This paper aims to present an assessment of user satisfaction of an innovative workplace design, otherwise known as flexible workplaces. Design/methodology/approach The study first sought to establish the level of flexibility of the workplace through the identification of flexibility criteria presented in a checklist format. In total, 29 criteria were identified and subsequently assigned weights by ten professionals. These professionals further assessed a case study office building through a walkthrough exercise to determine its level of flexibility. Furthermore, a post occupancy evaluation (POE) was conducted to assess the level of users’ satisfaction with functional performance elements. Questionnaire surveys were administered to 142 users, with a 63 per cent response rate. The feedback was analyzed and presented using the mean satisfaction index approach. Findings The results showed that the total flexibility achieved by the facility is 67.63 per cent, which is considered...

PurposeThe purpose of this paper is to develop a comprehensive list of key performance indicators (KPIs) that can be employed in determining the functional performance of academic and research laboratory facilities.Design/methodology/approachThe study employed a two-phase approach. First, a thorough literature review was conducted to identify potential KPIs specific to the performance of laboratory facilities. This was followed by an assessment of the KPIs by 12 respondents including 6 professionals and 6 users. The KPIs were arranged in the form of a questionnaire survey containing response columns for agree/disagree, and importance rating scales for evaluation. The relative importance index values were also computed.FindingsThe result of the study was a comprehensive list of 161 KPIs classified into nine categories including: space, access/circulation, utilities and waste, environmental conditions, furniture, appearance/finishes/image, communications, storage within the space and ...

Purpose The purpose of this paper is to present an assessment of the challenges to the implementation of building management systems (BMS) in Saudi Arabia, during the life cycle of building projects. Design/methodology/approach Review of literature and interviews were conducted with professionals to identify and synthesize the challenges to the implementation of BMS in Saudi Arabia. This formed the basis of three questionnaire surveys that were designed utilizing a five-point Likert scale, and consisted of 32 challenges. The surveys were assessed by representatives of architectural/engineering (A/E) firms, BMS installation sub-contractors and facilities managers to calculate the effect index of each challenge. Findings The top influential challenges pertaining to the briefing and design phase includes “inappropriate selection of the BMS,” “inappropriate selection of the A/E team”; installation and final acceptance phase includes “inappropriate selection of sub-contractors to install...

The construction industry, for many decades, has been underperforming in terms of the success of project delivery. Construction delays have become typical of many construction projects leading to lawsuits, project termination, and ultimately dissatisfied stakeholders. Experts have highlighted the lack of adoption of modern technologies as a cause of underproductivity. Nevertheless, the construction industry has an opportunity to tackle many of its woes through Construction 4.0, driven by enabling digital technologies such as machine learning. Consequently, this paper describes a framework based on the application of machine learning for delay mitigation in construction projects. The key areas identified for machine learning application include "cost estimation", "duration estimation", and "delay risk assessment". The developed framework is based on the CRISP-DM graphical framework. Relevant data were obtained to implement the framework in the three key areas identified, and satisfactory results were obtained. The machine learning methods considered include Multi Linear Regression Analysis, K-Nearest Neighbours, Artificial Neural Networks, Support Vector Machines, and Ensemble methods. Finally, interviews with professional experts were carried out to validate the developed framework in terms of its applicability, appropriateness, practicality, and reliability. The main contribution of this research is in its conceptualization and validation of a framework as a problem-solving strategy to mitigate construction delays. The study emphasized the cross-disciplinary campaign of the modern construction industry and the potential of machine learning in solving construction problems.

The construction industry is witnessing a rapid rise in tall building projects due to an anticipated urban population explosion. However, this building typology has been subject to time overruns and total abandonment due to an underestimation of the project duration. Consequently, this paper presents the development of a model to predict the construction duration of tall building projects. In developing the model, a suite of machine learning algorithms was adopted including Multi-Linear Regression Analysis (MLRA), k-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Ensemble Methods. Thus, twelve models were developed in the process, and the most efficient model was selected. The procedure described in this study presents researchers and practitioners with a strategy to enhance the time performance of tall building projects through the adoption of modern digital technologies such as machine learning. The proposed model was based on an ensemble method using ANN as the combiner, with a Correlation Coefficient (R 2) of 0.69, Root Mean Squared Error (RMSE) of 301.72, and Mean Absolute Percentage Error (MAPE) of 18%.

The construction industry is recognized as one of the most hazardous industries globally. Moreover, the rising trend of urbanization in many developing countries has ushered in a new era of high-rise construction, thus increasing the frequency of hazards related to working at height. Accident records in Saudi Arabia reveal that the construction industry accounted for 46.4% of industrial accidents, while fall-related injury accounted for 27% of the recorded injuries. Scaffolding is the most common access equipment used to work at height. Thus, the first stage in controlling the risks of falls from a height may be to identify the causes of scaffold accidents. This study presents 36 causes of scaffold accidents classified into five relevant groups, which have been identified through a thorough review of the extant literature. Additionally, the causes have been arranged in a survey designed based on a Likert scale of importance. Subsequently, 120 copies were administered to construction professionals in the Eastern Province of Saudi Arabia, with a 75% response rate. The Relative Importance Index (RII) was adopted to analyze the feedback. The results revealed that the top three causes of scaffold accidents include: "insufficient bracing/anchorage" (RII of 0.927), "scaffolding erected by incompetent professionals" (RII of 0.926), and "missing/faulty guardrails" (RII of 0.919). This study is of potential benefit to concerned stakeholders in the construction industry.

The construction industry has suffered for many years from productivity losses such as delays. The continual growth in complexity of construction projects such as highrise buildings in the 21st century further aggravates the issue. Thus, the construction industry continually seeks for smarter and intelligent ways to solve existing productivity issues. This study presents a comparison of an early model (Bromilow’s Time-Cost Model) to a more contemporary approach (k-Nearest Neighbors) in predicting the construction duration of high-rise buildings. The performance of both methods have been compared by means of the correlation coefficient, root mean squared error (RMSE) and mean absolute percentage error (MAPE). Interestingly, the results showed that the predictive capability of the BTC model was comparable to that of KNN. Though, KNN performed better with correlation coefficient (0.83), RMSE (3.45) and MAPE (22.04%) compared to the BTC model with correlation coefficient (0.81), RMSE (5.05) and MAPE (22.73%) respectively. Despite the predictive capabilities of the two techniques investigated in this study, future research may seek to explore the capabilities of more powerful machine learning techniques, as well as a variety of case studies.

High-rise buildings, which have become a significant part of the urban habitat, is particularly notorious for their delayed completion times. Though, there exists a plethora of studies on construction delays, the problem however is insufficient research on prescriptive methods to mitigate delays. Thus, this study sought to employ Machine Learning (ML) techniques to learn from historical data on high-rise construction to forecast potential delay times. An input data containing 9 features, and 12 cases was used. Initially five feature sets were built based on the recursive feature elimination process. Further to that was the classification process that employs the following ML techniques: Multi-Linear Regression Analysis (MLRA), k-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), and Support Vector Machines (SVM) to determine delay times. The predictive performance of these techniques was measured using their correlation co-efficient (R2) and their Root Mean Squared Errors (RMSE). The best three models according to the ML techniques used was SVM with 2 features (R2 0.56, RMSE 1.6), ANN with 2 features (R2 0.49, RMSE 1.83), and KNN with all features (R2 0.46, RMSE 1.71). To seek improvement of the predictive performance of the models developed, the three best performing models were combined using fixed and trained rules. The results showed an improvement for a fixed rule based on the minimum values with (R2 0.59, RMSE 1.65). The study has significant implications in the risk management process of high-rise projects to avoid delays. The originality is evident in that this is the first study that employs ML in predicting construction delay times.

The rise of tall buildings in urban centres across the globe has been attributed to the need to create more urban space for an imminent population explosion and urbanization crisis. Despite the potential of this building typology as a sustainable alternative to urban design, it has become notorious for being delayed, and sometimes abandoned. The research domain is saturated with numerous studies on the causes of construction delays, however inadequate effort has been channelled towards the development of prescriptive tools with the potential to mitigate construction delay. The desired solution is one that would employ innovative methods to arrive at problem solving strategies for the ultimate purpose of delay mitigation. Today, the fourth industrial revolution (IR 4.0) offers the construction industry a unique opportunity to solve its many woes, such as delays, through leveraging the capabilities of digital technologies such as artificial intelligence and machine learning. Thus, it is the purpose of this book to describe a delay mitigation framework proposed for tall building projects based on the application of machine learning. The application of machine learning is considered in three major areas of project delay risk mitigation including "reliable cost estimates", "reliable duration estimates", and "delay risk assessment". Interestingly, the concept of the delay mitigation framework can be extended to other project types, besides tall building projects.