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Article
Publication date: 5 October 2023

Babitha Philip and Hamad AlJassmi

To proactively draw efficient maintenance plans, road agencies should be able to forecast main road distress parameters, such as cracking, rutting, deflection and International…

Abstract

Purpose

To proactively draw efficient maintenance plans, road agencies should be able to forecast main road distress parameters, such as cracking, rutting, deflection and International Roughness Index (IRI). Nonetheless, the behavior of those parameters throughout pavement life cycles is associated with high uncertainty, resulting from various interrelated factors that fluctuate over time. This study aims to propose the use of dynamic Bayesian belief networks for the development of time-series prediction models to probabilistically forecast road distress parameters.

Design/methodology/approach

While Bayesian belief network (BBN) has the merit of capturing uncertainty associated with variables in a domain, dynamic BBNs, in particular, are deemed ideal for forecasting road distress over time due to its Markovian and invariant transition probability properties. Four dynamic BBN models are developed to represent rutting, deflection, cracking and IRI, using pavement data collected from 32 major road sections in the United Arab Emirates between 2013 and 2019. Those models are based on several factors affecting pavement deterioration, which are classified into three categories traffic factors, environmental factors and road-specific factors.

Findings

The four developed performance prediction models achieved an overall precision and reliability rate of over 80%.

Originality/value

The proposed approach provides flexibility to illustrate road conditions under various scenarios, which is beneficial for pavement maintainers in obtaining a realistic representation of expected future road conditions, where maintenance efforts could be prioritized and optimized.

Details

Construction Innovation , vol. 24 no. 1
Type: Research Article
ISSN: 1471-4175

Keywords

Article
Publication date: 5 March 2018

Moza Tahnoon Al Nahyan, Yaser E. Hawas, Mohsin Raza, Hamad Aljassmi, Munjed A. Maraqa, Basil Basheerudeen and Mohammad Sherif Mohammad

The purpose of this paper is to present a framework to devise a system for ranking of traditional project delivery methods, regarding their suitability, against the varying levels…

Abstract

Purpose

The purpose of this paper is to present a framework to devise a system for ranking of traditional project delivery methods, regarding their suitability, against the varying levels of mega project attributes.

Design/methodology/approach

The proposed system employs input and output interfaces and a granular (fuzzy rule base) component for estimating the subjective levels of risks, opportunities, and constraints and then mapping them to a decision matrix. A questionnaire has been designed (using the SurveyGizmo® platform) to collect the perceptions of the various project stakeholders and use them. A total of 127 stakeholders completed the survey form in full.

Findings

The survey data were used to calibrate the fuzzy logic model of the granular component. The envisioned system computes, for each possible delivery method, an index that reflects the suitability (of the corresponding delivery method) on an ordinal scale.

Originality/value

The devised decision support system is likely to lessen the dependency of “accurate decision” on “the experience of the decision-makers.” It will also enable ranking the various project delivery methods based on the various project and stakeholder attributes that are likely to affect the project risks, opportunities and constraints.

Details

International Journal of Managing Projects in Business, vol. 11 no. 1
Type: Research Article
ISSN: 1753-8378

Keywords

Content available
Article
Publication date: 5 March 2018

Nathalie Drouin

Abstract

Details

International Journal of Managing Projects in Business, vol. 11 no. 1
Type: Research Article
ISSN: 1753-8378

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