Causes of delay in the global construction industry: a meta analytical review

Sanni-Anibire, Muizz O.; Zin, Rosli Mohamad; Olatunji, Sunday O.

doi:10.1080/15623599.2020.1716132

Cited by 79 publications

(63 citation statements)

References 60 publications

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“…This may indicate that planning and delay issues have been solved to an acceptable degree in developed countries and are no longer considered worthy of further study. Nevertheless, as concluded by Sanni-Anibire et al [9], the causes of delay can be country/location and project specific, thus justifying country-specific research studies.…”

Section: Introductionmentioning

confidence: 98%

“…Research into delay factors for the construction industry can be found from as early as 1985, with 1 to 3 articles being published yearly until 2006, and 4 to 8 thereafter, with a spike of 18 published in 2017 [7]. In fact, three concurrent studies published in 2020 carried out a meta-analytical review of the relevant literature claiming to determine global causes of delay in the construction industry [8][9][10]. More specifically, 47 research articles, yielding 1057 different causes of delays, were examined by Viles et al [10].…”

Section: Introductionmentioning

confidence: 99%

“…Even though Sanni-Anibire et al [9] identified 93 related articles, only 11 met their research criteria and were considered in their meta-analysis. In total, 36 global delay causes were identified, and their relative importance indices (RII) were compared.…”

Section: Introductionmentioning

confidence: 99%

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Delay Risk Assessment Models for Road Projects

Antoniou

2021

Systems

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Significant universal research regarding causes of delay in road projects has been carried out based on expert opinion. This study classifies and standardizes all road construction delay factors found in the literature by developing a common risk breakdown structure (RBS) to allow for comparison between real project delay factors, and the study also proposes two delay risk assessment models (DRAMs) based on delay information from 120 real projects constructed in a similar environment with akin procurement and contract policies. The first calculates the risk priority number (RPN) and the second applies the Technique for Order Preference by Similarity to Ideal Situation (TOPSIS) multi-criteria decision-making method (MCDM). The results showed that four specific delay factors dominate in Greek road construction for which relevant mitigation proposals are made. The proposed DRAMs, while calibrated for application in Greece, can be adapted to any construction environment for which real project data is available to provide a tool for transferring experience from past projects to future projects and from accomplished to novice public client decision makers.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Delay Risk Assessment Models for Road Projects

Antoniou

2021

Systems

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“…These projects are however characterized by uncertainties that affect the success of the project, usually expressed in cost, time, and quality [1,2]. Experts are of the opinion that large variances in the estimated and actual duration of construction projects due to underestimation is one of the prevalent problems in the industry [3]. Bromilow [4] suggested that only one-eighth of building contracts were completed within the scheduled completion dates and that the average time overrun exceeded 40%.…”

Section: Introductionmentioning

confidence: 99%

“…Interestingly, the Council on Tall Buildings and Urban Habitat, CTBUH [6] in its report "Dream Deferred: Unfinished Tall Buildings" noted the alarming rate of increase of "never completed" tall buildings. Previous researchers have suggested that a reliable prediction of the duration of construction projects is crucial to avoiding construction delays [7,8,3,9]. Traditional methods such as the Critical Path Method (CPM) or Program Evaluation Research Task (PERT) have been shown to consistently underestimate the actual project duration [10].…”

Section: Introductionmentioning

confidence: 99%

Developing a machine learning model to predict the construction duration of tall building projects

Sanni-Anibire¹,

Zin²,

Olatunji³

2021

JCEMI

Self Cite

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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%.

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A Bibliometric Analysis of Sustainable Construction Practices—Implication on Construction Productivity

Adebowale

Agumba

2022

Lecture Notes in Civil Engineering

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Causes of delay in the global construction industry: a meta analytical review

Cited by 79 publications

References 60 publications

Delay Risk Assessment Models for Road Projects

Delay Risk Assessment Models for Road Projects

Developing a machine learning model to predict the construction duration of tall building projects

A Bibliometric Analysis of Sustainable Construction Practices—Implication on Construction Productivity

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