Developing a rehabilitation framework to enhance decision-making processes for engineers and stakeholders while promoting sustainable practices in the built environment

Abstract

In the context of aging structures, rapid urbanization, structural degradation, and environmental and socio-economic constraints, renovation and retrofitting have become indispensable for sustainable development. Retrofitting can be more challenging than new construction in some cases, as it involves significant uncertainty related to the original and current structural conditions, regulatory compliance, stakeholder expectations, and long-term performance. The complexity of such projects can lead to poor decision-making, cost overruns, schedule delays, and performance and longevity issues. This raises the question of how technology enhances our data collection and diagnostic process in building retrofitting, and to what extent it is adopted in the Lebanese construction sector. Are the existing and used tools in Lebanon adapted to emerging technologies such as BIM, artificial intelligence, and machine learning? How can a strict framework impact the cost and the schedule of a project compared to conventional structural assessment and retrofitting processes? This research presents a comprehensive renovation framework designed to support engineers in decision-making, reassure stakeholders, and encourage the adoption of advanced sustainable renovation techniques. The framework introduces innovative technologies and AIdriven analysis tools to enhance the reliability of structural assessments while incorporating sustainable practices into technical and managerial choices. The methodology involves multidisciplinary research, beginning with a systematic review of available rehabilitation techniques and sustainable construction methods, to identifying and addressing existing gaps. A structured rehabilitation framework is developed, integrating structural assessment with modular and prefabricated renovation solutions. The results will be analyzed through a life cycle assessment and a cost-effective analysis, conducted in accordance with international standards. A case study in Beirut demonstrates the application of the proposed method. Based on the results, the incorporation of artificial intelligence technology greatly increases efficiency by making the on-site inspection process more efficient, eliminating the need for corrections by hand, and increasing the efficiency and accountability of the rehabilitation process. In addition, the use of artificial intelligence diagnostics and modular construction technology decreased project time by about 45% because of better coordination and faster on-site work facilitated by modular construction techniques. . Results also showed that the modular retrofitting approach reduces embodied energy and decreases construction and demolition waste by up to 60% compared to the traditional method, thereby enhancing the built environment, increasing durability, and reducing total costs in the long run. This study contributes to academic knowledge by filling a gap in the literature on more sustainable rehabilitation strategies, providing practical value that could be beneficial to engineers, project managers, and policymakers. The proposed framework will support more sustainable, resilient, and informed rehabilitation practices, particularly in light of the current situation in Lebanon and many countries worldwide.