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Pavement Infrastructure Asset Management Using Clustering-Based Ant Colony Optimization

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Evolutionary Data Clustering: Algorithms and Applications

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

Pavement asset management comprises of all the activities involved in the planning stage and design stage followed by execution involving inventory, resource allocation, and construction activities of a pavement structure. It also comprises of maintenance activities of the pavement infrastructural system during the design life supported by continuous rehabilitation of pavement sections whenever and wherever necessary. To achieve the above goals efficiently and effectively, management strategies and tools are required which help the planners and decision-makers choose the optimum combination of activities and resources, based on defined multi-objective setups. Ant colony optimization (ACO) technique offers one such option to improve the efficiency of decision-making and clustering analysis in pavement asset management. It is inspired by the natural behavioral system of ants and its probabilistic formulations which lead to the selection of optimum strategies. It extends the scope of planning through a feedback mechanism in the management of pavement systems. In this book chapter, the need for the management of pavement assets is deliberated, followed by the detailed theoretical background of ant colony optimization and its possible application in the pavement asset management is deliberated. A framework is proposed for the application of ACO in pavement management system. Further, an ACO based clustering approach is deliberated to choose optimum bids for asphalt pavement construction projects based on time of completion, total cost, and quality of construction.

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Author information

Authors and Affiliations

  1. Formerly, Indian Institute of Technology Delhi, New Delhi, India

    Saqib Gulzar & Hasnain Ali

Authors
  1. Saqib Gulzar
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  2. Hasnain Ali
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Corresponding author

Correspondence to Saqib Gulzar .

Editor information

Editors and Affiliations

  1. King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan

    Ibrahim Aljarah

  2. King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan

    Hossam Faris

  3. Center for Artificial Intelligence Research and Optimization, Torrens University Australia, Brisbane, QLD, Australia

    Seyedali Mirjalili

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Gulzar, S., Ali, H. (2021). Pavement Infrastructure Asset Management Using Clustering-Based Ant Colony Optimization. In: Aljarah, I., Faris, H., Mirjalili, S. (eds) Evolutionary Data Clustering: Algorithms and Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-4191-3_10

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