Ann-Based Decision Model for the Reuse of Vacant Buildings in Urban Areas

Abstract

Because of global urbanization and sustainable development trends, reusing vacant buildings is a crucial strategy employed in urban development and management. Reusing and adjusting the future service values of unused buildings to extend building life cycles is a sustainable approach that benefits society, the economy, and the environment. However, repurposed spaces are easily re-discarded because a comprehensive system and operational plan for assessing the effects of building reuse remains unestablished. The research framework adopted in this study was based on the seven factors of the AdaptSTAR model; assessment criteria for building reuse were then created. In addition, 62 types of reused building cases in Taiwan were investigated and a decision model for reuse type prediction and business strategy was constructed on the basis of artificial neural networks. The results indicated that the proposed decision model yielded a reuse type accuracy of 89% and a business strategy accuracy of 78%. This systematic approach can be adjusted according to local conditions and applied as an effective decision support tool.

The construction client satisfaction model: a PLS-SEM approach

“…What differentiates leading construction companies from others is service excellence, which improves productivity, maintains client loyalty, and creates positive word-of-mouth advertising, all of which can create advantages in the competitive business environment. Due to its significance, client satisfaction has received broad attention in the literature (Barret 2000;Torbica, Stroh 2001;Maloney 2002;Yasamis et al 2002;Yang, Peng 2008;Kärnä et al 2009;Li et al 2013;Hsu, Juan 2016;Aliakbarlou et al 2017;Hadidi et al 2017). For example, construction client satisfaction was perceived to be the most significant determinant of success in the Turkish construction industry context (Arslan, Kivrak 2009).…”

Property Management Enabled by Artificial Intelligence Post Covid-19: An Exploratory Review and Future Propositions

“…Furthermore, Li et al (2012) developed a novel model to investigate the effects of integrated property and rail development and presented a heuristic solution algorithm. Hsu and Juan (2016) established AdaptSTAR model based on artificial neural networks (ANN) for the reuse of vacant buildings in urban areas. Nevertheless, for a long time, the construction industry has been timid to implement new technologies in property management, and it hence requires an AI-based decision-support system that tackles the risks and market uncertainty (Berggren et al, 2021).…”

Current Status and Emerging Trends on the Adaptive Reuse of Buildings: A Bibliometric Analysis

“…It is expected that exploring and co-building the shared spaces that have been left underused during this pandemic will take a different role in the cities in the future. Moreover, the opportunity is presented to increase research into policies, regulations and stakeholder's involvement in the industry to explore the possibility for adaptation across all the lifecycle of the building because sometimes intentions to building reuse for new purposes might be challenged by local building regulations, zoning and codes with other legal requirements which might obstruct the reuse potential [74].…”