Artificial intelligence applications for proactive environmental monitoring and asset management

Abstract

Two research studies have been implemented to explore the potential of applying artificial intelligence (AI) technologies in works projects and maintenance work of the Drainage Services Department (DSD) for enhancing the efficiency related to environmental monitoring and structural inspection, referred to as the AIEIA and AIBIM projects, respectively. In the AIEIA project, AI technologies were explored to assist observing bird behaviour that would likely be influenced by nearby DSD construction projects. A domain randomisation-enhanced model was built to detect great egrets and little egrets at Penfold Park, Hong Kong, achieving a mean average precision of 87.65%. The detection result was used to analyse the Penfold Park egretry behaviour. In the AIBIM project, AI technologies were used to facilitate the condition assessment of concrete defects in sewage treatment facilities. A classifier was developed with supervised learning for concrete defect detection, attaining recalls of 86.2% and 89.9% for the cracking and spalling classes. Another concrete defect anomaly detector was built using unsupervised learning, achieving balanced results with F2 measures of 85.2% and 76.0% for the cracking and spalling classes. The two research studies render valuable experience for the DSD to integrate AI-enabled analytics into future work to continuously improve the drainage services in Hong Kong.