Poudel, O and Assaad, R H (2025) A real-time intelligent acoustic IoT-enabled embedded construction site monitoring and alert system: Integrating deep learning-based machine-listening algorithms, edge computing, and cloud computing. Journal of Construction Engineering and Management, 151(7): 04025075, ISSN 0733-9364
Abstract
Acoustic-based construction site monitoring approaches have attracted recent interest due to their advantages compared to other methods. Previous relevant studies have many limitations, including narrowly focusing on sounds related to a specific/limited application without offering comprehensive monitoring capabilities of various construction site-related activities; tackling the software-side (or computational aspects) with limited work on hardware IoT-enabled embedded devices that could be used for real-time data collection, analysis, and edge computation; and lacking cloud-computing and visualization capabilities needed to improve data accessibility, storage, interpretation, and communication. This paper addresses these research gaps by developing a real-time intelligent IoT-enabled embedded acoustic-based sensing system for construction site monitoring by integrating machine listening techniques based on deep learning algorithms, edge computing based on Wi-Fi and bluetooth low energy (BLE)-enabled embedded systems, and cloud computing based on Amazon Web Services EC2. This study designed an automated monitoring system that uses convolutional recurrent neural networks to interpret construction site audio data with an accuracy of 89.13% across 14 classes of various audio related to different construction site aspects categorized into equipment and work activities, weather/environmental conditions, possible hazards, and workforce-related. The paper also developed a smartphone application to facilitate immediate and targeted alerts to relevant stakeholders. Finally, the proposed approach was tested in various construction workshops and environments. This paper's contributions are reflected by offering an unprecedented technological workflow/architecture that integrates software and hardware innovative advancements in audio-based monitoring systems of construction jobsites across various types of sounds. The paper also adds to the body of knowledge by developing an integrated system of real-time IoT-enabled acoustic capabilities, utilizing modern machine listening techniques powered by cloud and edge computing to improve current construction-site surveillance systems. This paper has the promise to change the way construction sites are monitored and managed, thereby contributing to enhanced safety and efficiency in the construction industry.
| Item Type: | Article |
|---|---|
| Index terms: | deep learning, real-time data, efficiency, accessibility, low energy, workshop, integrated system, construction industry, monitoring, hardware, construction site, neural network, accuracy, cloud computing, workflow, visualization, body of knowledge, weather, environmental conditions, computing, site surveillance system, computation |
| Subjects: | environmental science, knowledge management, construction type, health safety and environment, inclusive design, artificial intelligence, energy systems, work location, control systems, design methods, computer hardware, computing systems, air quality, computational methods, industry analysis, digital infrastructure, data management, professional development, management, design practice, performance management |
| Topics: | Research Practice, Information Management, Business Strategy, Construction Technology, Design Practice, Digital Applications, Site Management, Health and Safety, Sustainability, Quality Management |
| Descriptive scope: | 3 PCT |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here