Predicting Military Construction Project Time Outcomes Using Data Analytics

Innocent, Max; Wasek, James S.; Franz, Anna

doi:10.1080/10429247.2018.1490995

Cited by 6 publications

(5 citation statements)

References 28 publications

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“…Therefore, sanitization in big projects should be done in such a way as not to jeopardize the anticipated EP outcomes of the project. This finding contradicts the assertion of Innocent et al (2018) that due to the size of big projects, they are more susceptible to schedule overruns which eventually leads to poor EP. It also contradicts the position of Woolley et al (2020) who submits that the restricted nature of the feedback mechanism in big projects places them at a greater economic disadvantage than smaller projects.…”

Section: Discussioncontrasting

confidence: 76%

Section: Discussioncontrasting

confidence: 76%

“…Many studies have revealed that there is a positive relationship between project size and the level of complexity in the construction project (Patanakul et al, 2016). Also, Innocent et al (2018) opines that big construction projects are more prone to schedule overrun than smaller projects. However, it should be noted that previous studies have been able to show that schedule overrun is usually accompanied by cost overruns which have adverse effects on EP.…”

Section: Project Size As a Moderatormentioning

confidence: 99%

“…, 2016). Also, Innocent et al. (2018) opines that big construction projects are more prone to schedule overrun than smaller projects.…”

Section: Literature Review and Hypothesis Developmentmentioning

confidence: 99%

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Moderating effect of project size on the relationship between COVID-19 safety protocols and economic performance of construction projects

Onubi

Hassan

Yusof

et al. 2022

ECAM

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PurposeThe COVID-19 health crisis has brought about a set of extra health and safety regulations, and procedures to the construction industry which could influence projects' economic performance (EP). The aim of this paper is to examine the effect of adopting COVID-19 safety protocols on construction sites on the economic performance (EP) of construction projects.Design/methodology/approachEmploying the survey method using a structured questionnaire, data were collected from small- and large-sized construction projects in Nigeria and analysed using partial least squares structural equation modelling (PLS-SEM) technique.FindingsThe findings reveal that job re-organization and sanitization have negative significant effects on EP, while social distancing and specific training have no effect on EP. Furthermore, project size moderates the relationship between job re-organization, sanitization, specific training and EP with the stronger effect on the relationships observed in big projects, except for the relationship between sanitization and EP where the moderating relationship is stronger in small projects. However, there is no significant moderating effect of project size on the relationship between social distancing and EP.Practical implicationsAs construction project sites continue to operate amidst strict safety protocols, this study offers theoretical and practical insights on how construction projects can adhere to the safety protocols while performing economically.Originality/valueThe originality of this study's findings stems from the fact that it is among the first to provide greater insight on how construction projects have fared economically considering the impact of the various COVID-19 protocols.

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Section: Discussioncontrasting

confidence: 76%

Section: Discussioncontrasting

confidence: 76%

Section: Project Size As a Moderatormentioning

confidence: 99%

“…, 2016). Also, Innocent et al. (2018) opines that big construction projects are more prone to schedule overrun than smaller projects.…”

Section: Literature Review and Hypothesis Developmentmentioning

confidence: 99%

See 2 more Smart Citations

Moderating effect of project size on the relationship between COVID-19 safety protocols and economic performance of construction projects

Onubi

Hassan

Yusof

et al. 2022

ECAM

View full text Add to dashboard Cite

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“…e prediction types of cost indexes mainly include the prediction of tender price index (TPI) [7] and Engineering News-Record (ENR) construction cost index (CCI) [8]. Among them, the construction cost prediction data mainly comes from expert judgment or historical data of similar projects [9], which lacks authority and persuasion. However, the data source of cost indexes prediction is generally a dynamic release from authoritative institutions.…”

Section: Introductionmentioning

confidence: 99%

Cost Index Predictions for Construction Engineering Based on LSTM Neural Networks

Dong

Yuan

Guo

2020

Advances in Civil Engineering

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In recent years, the cost index predictions of construction engineering projects are becoming important research topics in the field of construction management. Previous methods have limitations in reasonably reflecting the timeliness of engineering cost indexes. The recurrent neural network (RNN) belongs to a time series network, and the purpose of timeliness transfer calculation is achieved through the weight sharing of time steps. The long-term and short-term memory neural network (LSTM NN) solves the RNN limitations of the gradient vanishing and the inability to address long-term dependence under the premise of having the above advantages. The present study proposed a new framework based on LSTM, so as to explore the applicability and optimization mechanism of the algorithm in the field of cost indexes prediction. A survey was conducted in Shenzhen, China, where a total of 143 data samples were collected based on the index set for the corresponding time interval from May 2007 to March 2019. A prediction framework based on the LSTM model, which was trained by using these collected data, was established for the purpose of cost index predictions and test. The testing results showed that the proposed LSTM framework had obvious advantages in prediction because of the ability of processing high-dimensional feature vectors and the capability of selectively recording historical information. Compared with other advanced cost prediction methods, such as Support Vector Machine (SVM), this framework has advantages such as being able to capture long-distance dependent information and can provide short-term predictions of engineering cost indexes both effectively and accurately. This research extended current algorithm tools that can be used to forecast cost indexes and evaluated the optimization mechanism of the algorithm in order to improve the efficiency and accuracy of prediction, which have not been explored in current research knowledge.

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Imprecise earned duration model for time evaluation of construction projects with risk considerations

Hamzeh

Mousavi

Gitinavard

2020

Automation in Construction

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Predicting Military Construction Project Time Outcomes Using Data Analytics

Cited by 6 publications

References 28 publications

Moderating effect of project size on the relationship between COVID-19 safety protocols and economic performance of construction projects

Moderating effect of project size on the relationship between COVID-19 safety protocols and economic performance of construction projects

Cost Index Predictions for Construction Engineering Based on LSTM Neural Networks

Imprecise earned duration model for time evaluation of construction projects with risk considerations

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