Development of a Decision Support System for the structural degradation analysis of RC buildings supported by user-reported data and modern Information Technologies

Exposure to aggressive environmental agents and aging can seriously harm reinforced concrete (RC) structures and affect the building’s service life. In particular, many buildings built during the extensive dissemination of RC construction technique present criticalities regarding performance and durability due to many issues: inadequate design of technical details, incorrect execution method, inadequate materials, improper maintenance or intrinsic limits and vulnerability of concrete. Consequently, management and monitoring of existing buildings is a complex issue for holders and administrators that are responsible for the building operability and user safety. On the other hand, large-scale degradation analysis and maintenance procedures present critical points: i) non-uniform building knowledge, ii) need of performing several inspections, iii) complexity of the diagnostics related to the subjectivity of the surveyor, iv) vastness and deployment of buildings in the region. Such issues become particularly complex for public or private administrators, who usually have limited resources and often cannot rely methodologies and tools to face the problem. This thesis proposes an innovative approach for detecting building criticalities. In order to achieve the new system, four synergistically related methods are suitably developed and listed as follows: i) a novel DSS methodology for buildings’ assessment supported by modern Internet of Things tools (smart devices) and Information Technologies; ii) an innovative Optimized AHP (O-AHP) based on a Mathematical Programming problem; iii) a calibration procedure based on the Tuutti model to consider the damage evolution of reinforcements’ corrosion and tune the KPIs; iv) a Test Site involving 131 building on the Valencian coasts to test the DSS. In particular, the proposed DSS includes an innovative software application (APP) implemented on mobile devices and a web service–based Quality Detection Platform (QDP) that stores and processes data. The APP allows the building users to report criticalities through photographic acquisition and to answer a specific questionnaire. Collected data are processed by the QDP, which quantifies critical issues through the set of key performance indices (KPIs). The DSS allows monitoring buildings at the regional scale and provides a classification of the most damaged buildings, which is useful for civil protection aims and for the prioritization of interventions.