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Abstract

The framework integrates advanced technologies to transforming traditional, reactive maintenance methods into proactive, data-driven strategies. The approach begins with extensive data collection using sensors such as laser scanners, HTLC multi-dimensional sensors, and Ground Penetrating Radar (GPR) to monitor surface and subsurface conditions of road networks and CO2 emissions in real-time. Data processing and management are handled through a combination of edge, fog, and cloud computing, enhancing data reliability and reducing latency. Blockchain technology is integrated to ensure data integrity and security. To achieve predictive maintenance, the framework uses Machine Learning (ML) models (Multiple Time Series Stacking (MTSS) and Artificial Neural Networks (ANNs)). They analyse the collected data to predict maintenance needs and optimize intervention strategies, thereby reducing the frequency and scale of repairs and conserving resources. The integration of Building Information Modeling (BIM) and Geographic Information Systems (GIS) further enhances the framework. This integration enables informed decision-making, aligning maintenance efforts with environmental sustainability goals.
This study uses a mixed-methods research approach, by gathering secondary data through an extensive literature review and validating the findings trough a questionnaire. The questionnaire distributed to professionals in the Architecture, Engineering, and Construction (AEC) industry. This approach validates the framework and demonstrates the transformative potential of DT technology in reducing carbon emissions and operational costs, setting a new standard for sustainable infrastructure management.

Course: Building Information Management - MSc - P2657FTC

Date Deposited: 2025-01-17

URI/permalink: https://library.port.ac.uk/dissert/dis14688.html