Optimization of Microphone Placement for Audio-based Modeling of Construction Jobsites

Heavy equipment is a crucial resource in the construction industry. Recent studies have shown that analyzing the sound patterns generated by construction machinery can be an effective way to monitor their performance and detect potential operational issues. However, construction jobsites are complex environments that require consideration of multiple factors when creating an audio-based model. To perform efficient audio-based modeling of jobsites, it is essential to optimize the number and placement of microphones to capture the sound emitted by all the operating machines and achieve optimal sound quality. To address this challenge, we developed two optimization methods: (a) an integer programming model that guarantees finding the optimal placement of microphones, and (b) an evolutionary programming model, a heuristic approach more suited to larger problem instances. We evaluated the effectiveness of these models in five different case studies from construction jobsites. Our results showed that the developed models require a reasonable number of microphones to achieve the desired sound quality, demonstrating their satisfactory performance. Notably, both approaches exhibited similar performance in terms of the required number of microphones needed to cover all the machinery.