A machine learning approach for predicting delays in construction logistics

Asadi, Ahmad; Alsubaey, Mohammed; Makatsoris, Charalampos

doi:10.1080/2287108x.2015.1059920

Cited by 24 publications

(18 citation statements)

References 14 publications

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“…Methods Results [30] Questionnaire survey, decision tree and Naive Bayes Accuracy of decision tree 79.41% is higher than Naive Bayes by 5.81% [31] Questionnaire and Bayesian decision tree…”

Section: Authormentioning

confidence: 99%

“…Few studies used AI models in risk prediction and classification. Asadi et al (2015) used a decision tree and a Naive Bayes model based on a questionnaire survey to predict delay in construction logistics. The authors evidenced the capacity of the decision tree has higher accuracy by 79.41% over the Naive Bayes model, which showed a lower accuracy value of 73.52% [30].…”

Section: Research Backgroundmentioning

confidence: 99%

“…Asadi et al (2015) used a decision tree and a Naive Bayes model based on a questionnaire survey to predict delay in construction logistics. The authors evidenced the capacity of the decision tree has higher accuracy by 79.41% over the Naive Bayes model, which showed a lower accuracy value of 73.52% [30]. Naji et al (2018) used a Bayesian decision tree model to predict the impact of contract changes on the time and quality performance of construction projects [31].…”

Section: Research Backgroundmentioning

confidence: 99%

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Prediction of Risk Delay in Construction Projects Using a Hybrid Artificial Intelligence Model

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Ali

Salih

et al. 2020

Sustainability

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Project delays are the major problems tackled by the construction sector owing to the associated complexity and uncertainty in the construction activities. Artificial Intelligence (AI) models have evidenced their capacity to solve dynamic, uncertain and complex tasks. The aim of this current study is to develop a hybrid artificial intelligence model called integrative Random Forest classifier with Genetic Algorithm optimization (RF-GA) for delay problem prediction. At first, related sources and factors of delay problems are identified. A questionnaire is adopted to quantify the impact of delay sources on project performance. The developed hybrid model is trained using the collected data of the previous construction projects. The proposed RF-GA is validated against the classical version of an RF model using statistical performance measure indices. The achieved results of the developed hybrid RF-GA model revealed a good resultant performance in terms of accuracy, kappa and classification error. Based on the measured accuracy, kappa and classification error, RF-GA attained 91.67%, 87% and 8.33%, respectively. Overall, the proposed methodology indicated a robust and reliable technique for project delay prediction that is contributing to the construction project management monitoring and sustainability.Sustainability 2020, 12, 1514 2 of 14 in terms of litigation, dispute and arbitration [5]. Delays are caused by many sources and factors such as the owner [6,7], designer [3,8], contractor [4,7], materials [4,7], project [7,8], labor [9] and external factors [3,10]. Literature ReviewThe prediction of project delay based on internal and external sources can help project managers to provide an accurate forecast of the project schedule, and this can assist a proactive management approach in the construction project [11]. Construction projects are dynamic and complex, included a huge number of project stockholders, feedback processes and non-linear relationships [12]. The existence of a delay problem is related to interdependent factors that affect the construction project and the complexity and uncertainty of construction activities. Thus, providing of an efficient tool for analyzing delay factors is key for estimating an accurate duration in construction projects [11].By recalling previous studies, Chan (2001) used regression analysis to identify time-cost relationships for building projects in Malaysia [2]. This approach was developed for managers and owners to estimate the average time that is required for project delivery. Chan and Chan (2004) performed multiple regression exercises to analyze data related to the time performance of construction projects [13]. The results indicated that multiple regression was used as a useful method to predict time performance in construction projects. Rezaie et al. (2007) used Monte Carlo analysis to investigate the effects of uncertainties on the schedule performance [14]. The results revealed that this method is a good tool to simulate the relationship of uncertainties of construct...

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“…Methods Results [30] Questionnaire survey, decision tree and Naive Bayes Accuracy of decision tree 79.41% is higher than Naive Bayes by 5.81% [31] Questionnaire and Bayesian decision tree…”

Section: Authormentioning

confidence: 99%

Section: Research Backgroundmentioning

confidence: 99%

Section: Research Backgroundmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Risk Delay in Construction Projects Using a Hybrid Artificial Intelligence Model

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Ali

Salih

et al. 2020

Sustainability

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“…Amazon web services (AWS) which offers robust cloud-based services for the distinct set of application solutions and project conditions, discussed about the viability of using the machine learning based solutions and effective project management. [11]…”

Section: Related Workmentioning

confidence: 99%

Comprehensive Project Management Framework using Machine Learning

2019

ijrte

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Among the key reasons for the project management failures, unrealistic project schedules and ineffective utilization of the project conditions are key challenges. Irrespective of the size of the project and nature of project, there is imperative need for ensuring project conditions are effectively planned and there is holistic structure in place for managing the projects. There are many online solutions too which provides significant insights in to the system and its practices. The emergence of AI and the machine learning models has created a paradigm shift in the way things are managed in the business and project functions. The decision-making related insights generated by the machine learning models can be a phenomenal impact for the organizations. It is evident from the depth of the study discussed in the report, that if effectively planned and right kind of machine learning models are chosen, there are prospective conditions for the models to be trained and an automated system be developed which can yield considerable results. Even for the implementation team, if there are too many ML models to be implemented as a process of detecting the right fit solutions and this study identifies the need for a more comprehensive and single-window system of machine learning based project management framework

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“…The delay is a situation when the owner and contractor mutually or separately contribute to the lack of completion of the project as specified in the original contract period [2]. Delays can be minimized when associated causes are clearly identified [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Critical Delaying Factors: Public Sector Building Projects in Gilgit-Baltistan, Pakistan

et al. 2018

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Project delays are a large and prevalent problem in the Gilgit-Baltistan construction industry, and delays can distinctly affect project duration, budget, and also community needs. The purpose of this study is to highlight and rank delaying factors in the Annual Development Programme public sector building infrastructure projects and examine them through a relative importance index. A total of 52 delaying factors were identified through a detailed literature review and categorized into eight major groups, and a pilot study with 16 experienced construction experts was conducted. A questionnaire survey was conducted to collect the importance of each factor from public construction experts. In total, 102 respondents participated in the survey. The study determined that difficulties in financing projects by contractors, delays in progress payments, dispute on land usage, improper project feasibility studies, award project to the lowest bid price, extreme weather conditions, inadequate contractor experience, and insufficient data collection and survey before design were among the top eight critical delaying factors. In addition, Spearman's rank correlation tests revealed that there was no difference in perception between owners and contractors. A comparison of the top five delay factors was done with eight preferred construction industries in Asia to validate the results of this study. The findings are likely to be a solid contribution to the Gilgit-Baltistan construction industry in mitigating future construction delays.

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A machine learning approach for predicting delays in construction logistics

Cited by 24 publications

References 14 publications

Prediction of Risk Delay in Construction Projects Using a Hybrid Artificial Intelligence Model

Prediction of Risk Delay in Construction Projects Using a Hybrid Artificial Intelligence Model

Comprehensive Project Management Framework using Machine Learning

Critical Delaying Factors: Public Sector Building Projects in Gilgit-Baltistan, Pakistan

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