Leveraging BIM and Big Data to deliver well maintained highways. Issue 13 (3rd October 2017)

• Record Type: Journal Article
• Authors: Aziz, Zeeshan; Riaz, Zainab; Arslan, Muhammad
• Is Part Of: Facilities. Volume 35:Issue 13/14(2017)
• Page Start: 818
• Page End: 832
• Publication Date: 2017-10-03
• Subjects: Big Data -- Facilities management -- BIM -- Asset management -- Data management -- Highways management; Facility management -- Periodicals; Plant engineering -- Periodicals; 658.2
• DOI: 10.1108/F-02-2016-0021
• Languages: English
• ISSNs: 0263-2772

Abstract

Abstract : Purpose: Effective management of highways requires management of diverse data sets including traffic volume data, roadway, and road edge and road-side data. Like all major infrastructure clients, highways administration authorities are under pressure to use such platforms for better management of data that, in addition to creating other opportunities, allows improved life cycle management of asset data and predictive analytics. This paper aims to review such opportunities and the value that can be generated through integrated life cycle data management by leveraging Big Data and building information modelling (BIM). Design/methodology/approach: A literature review is initially performed to systematically gather information to identify and understand BIM as a collaborative platform. Data management applications in other industries are also reviewed. Interviews were conducted and two industry workshops were organised to understand BIM implementation challenges within highways development projects and the role BIM can play in bridging inefficiencies resulting from loss of information at handover phases. The overall understanding lead to drawing up user needs, gathering system requirements and eventually a system architecture design to promote efficient information management throughout the asset lifecycle. Findings: It is observed that data from the design and construction phases of projects can be used to inform asset registers from an earlier stage. This information can be used to plan maintenance schedules. Moreover, it can also be integrated with data generated from numerous other sensors to develop a better picture of network operations and support key decision-making. Effective road network management involves collection and analysis of huge data from a variety of sources including sensors, mobiles, assets and Open Data. Recent growth in Big Data analytics and data integration technologies provides new opportunities to optimise operations of highways infrastructure. Research limitations/implications: The system architecture designed for this research is translated into a prototype system as a proof of concept. However, it needs to be tested and validated by end users to be transformed into a useful solution for the industry. Originality/value: This paper provides an enhanced understanding of new opportunities created to optimise operations of highways infrastructure using the recent growth in Big Data analytics and data integration technologies.