A BIM-based and data-driven approach for comprehensive façade inspection guidance in cities

Well-maintained building façades are essential for public safety. Although major cities in the U.S. have façade safety inspection programs to ensure façade safety, unfortunately, incidents/accidents and complaints about façade problems are continuously observed. In 2019 only, 24 accidents caused by falling debris resulted in six fatalities and 40 injuries across the U.S. Only in New York City (NYC), did the Department of Buildings (DOB) receive more than 1000 complaints about façade safety. These accidents and complaints indicate a need to improve the current façade safety inspection practices. By analyzing safety inspection program guidelines, historical inspection reports, interviews with experts in the field, and shadowing work with inspectors, I identified three specific challenges in the current façade safety inspection processes that require attention to reduce/eliminate these issues: (a) the current safety inspection practice relies on inspectors’ experience, leading to inconsistent and non-comprehensive façade safety inspection results across inspections conducted; (b) current safety inspection guidance is material-based (e.g., check cracks on concrete), while inspectors’ practice is component-based (e.g., check cracks on walls) leading to discrepancies on applicable defects to check on components; and (c) current static, scattered, and paper-based practice of capturing and reviewing façade safety inspection findings hinders getting a holistic view of defect types, associated components, locations, and safety ratings, resulting in omissions and/or inefficiencies in getting a holistic mapping of defects on spatial context of façades.To address the identified challenges, this research aims to develop a model-based approach, supported by data-driven analyses, that can provide comprehensive and component-based guidance on the information that is required for façade safety condition evaluation. For the development of such an approach, the research work I have completed includes (1) identifying a taxonomy of façade defects and component hierarchies and mapping relationships between them, providing a classification of defects possible to observe on façades and a hierarchy of subcomponents that are fundamental pieces that are missing towards generation of component-based and comprehensive inspection checklists; (2) using these taxonomies, defining a machine-learning based method to identify and analyze façade defect patterns spatially in cities and reporting back on defect patterns observed in NYC neighborhoods by implementing this method for NYC buildings that are under the inspection ordinance; (3) developing a object-oriented representation schema and reasoning mechanisms for generation of customized checklists through reasoning with building information models (BIMs) of façades and leveraging the taxonomies and applicability relationships identified between the two (i.e., mapping relationships); and (4) evaluating integrated visualization (i.e., semantic information visualization over spatial information) techniques on inspectors’ efficiency and accuracy to get a holistic understanding of façade conditions and suggesting integrated visualization techniques that improve both efficiency and accuracy. The research findings have been validated in terms of (1) the comprehensiveness of the identified taxonomies for façade safety inspection and the accuracy of the mapping relationships; (2) the generality and extensibility of the developed object-oriented representation schema in generating customized inspection checklists for façade safety inspection guidance across different façade material types (e.g., concrete, brick masonry, stone) and building façade component hierarchies (e.g., with/without balconies, with/without parapets); and (3) the accuracy and efficiency of integrated visualization techniques in improving inspectors’ understanding of façade conditions.The contributions of this research include: (1) the taxonomies of façade component hierarchies and defect types, as well as the mapping relationship between them, showing applicable defect types on any given component in the hierarchy; (2) a machine-learning-based method to enable spatial analysis of defect patterns in cities, and identified defect patterns observed in NYC neighborhoods by implementing this method for NYC buildings; (3) a façade safety inspection domain ontology and reasoning mechanisms for model-based checklist generation; and (4) identification of effective integrated visualization techniques in improving inspectors’ understanding of façade conditions in terms of accuracy and efficiency of their tasks.The practical implications of this research are for inspection companies and related city agencies (e.g., DOB). These include (a) utilizing the taxonomies and checklists generated from the automated model-based system for different types of façade materials to achieve a standard inspection practice across inspection companies. The checklists can be used as suggested guidelines to improve the inspection scope for that façade material to reduce omissions due to the inexperience of inspectors or discrepancies in company practices. The identified relationships between defect types and each façade component can help inspectors be aware of relevant defects to inspect given a component type, facilitating systematic and comprehensive data collection for FISP inspections; (b) providing a clear view of NYC buildings for NYC DOB and inspection companies about the defect patterns per neighborhood, and enabling an in-depth understanding of the façade safety condition in NYC and shedding light on what types of defects to prioritize in different neighborhoods in the city. DOBs of other cities can deploy the method developed in this research to achieve similar spatial maps of their cities with respect to frequently observed defect types to guide inspection processes; (c) utilizing the effective integrated visualization techniques identified in this research to get fast and accurate assessment of façade conditions, particularly for buildings that have previous inspection findings in earlier inspection cycles. Given that inspection companies can change over these inspection cycles, it will be effective to use these integrated visualization methods to understand the reported conditions in previous cycles by another company.