For highly and densely populated cities like New York City, façade inspection is a mandatory routine every 5 years for buildings that have more than 6 floors. The current inspection is mainly based on visual checks, and the results are based on the inspectors’ experience. The objectives of this research are (1) to identify the required information for the decision-making of façade inspection and (2) to support the façade inspection process with a model-based generation of comprehensive checklists and flexible visualization of inspection findings.
Shi, Z., & Ergan, S. (2021). An Ontology Towards BIM-based Guidance of Building Façade Maintenance. In ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction (Vol. 38, pp. 901-908). IAARC Publications.
Shi, Z., & Ergan, S. (2020). Towards point cloud and model-based urban façade inspection: Challenges in the urban façade inspection process. In Construction Research Congress 2020: Safety, Workforce, and Education (pp. 385-394). American Society of Civil Engineers (ASCE).
Shi, Z., Park, K., & Ergan, S. (2020). Towards a comprehensive façade inspection process: An NLP based analysis of historical façade inspection reports for knowledge discovery. In ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction (Vol. 37, pp. 433-440). IAARC Publications.
Shi, Z., and Ergan, S. (2019). “”, 4th International Conference on Civil and Building Engineering Informatics, ICCBEI, Sendai, Japan, November 7-8, 2019. (abstract submitted).
Shi, Z., and Ergan, S. (2018). “Leveraging point cloud data for detecting building façade deteriorations caused by neighboring construction.” 5th International Project and Construction Management Conference (IPCMC), Cyprus, November 16-18, 2018.