![]() ![]() Research on estimating sound-source locations at construction sites is essential for applying monitoring systems based on sound data. While most previous studies on sound data in the construction sector are focused on sound pattern analysis, studies on estimating sound-source locations are insufficient. As sound data are also considered management targets rather than noise at construction sites, studies on data pattern analysis have recently been conducted to classify equipment sounds. This study focuses on sound data identification at construction sites to potentially improve risk management, either independently or combined with other technologies at construction sites (as mentioned above), even though sound-data-based position estimation systems that analyze the acoustic signals collected by microphone arrays have been actively used in other fields, such as automobiles, maritime navigation, robots, and military applications. However, they cannot monitor the work conditions or safety. While the above-mentioned sensor-based systems are used to collect the location information of major materials that need to be managed, they can also analyze the status and activity data of specific equipment and construction workers. ![]() Finally, IMU sensors are attached to objects such as construction equipment to indicate movement and movement paths with gyroscopes to prevent collisions and improve their mobility. For example, beacon devices are attached to heavy equipment at construction sites to monitor the location and condition of the equipment to increase the safety and work efficiency of workers at construction sites. Bluetooth technology uses a beacon device to identify location information based on the strength of the signal. RFID technology can identify location information by reading tags associated with resources, such as for labor access control, material logistics, and equipment control. ![]() Various sensor-based technologies, such as radio frequency identification (RFID), Bluetooth, and inertial measurement units (IMUs) already exist. Various innovative technologies and measures are being applied at construction sites, considering that location information collection is essential for real-time monitoring. It can also be utilized to monitor construction productivity by categorizing and locating the machinery-related sound sources. The system can categorize an accidental or abnormal sound and locate the sound source for safety management at the construction site. Furthermore, CSLF is applicable on the sound data categorization-related research. These results show that the CSLF can effectively identify sound locations at construction sites within an arm’s length. The exterior tests conducted at an actual construction site showed average x-, y-, and z-axis errors of 0.19, 0.21, and 0.24 m, respectively. The interior tests showed that the average error was less than 0.5 m for each axis which is 0.26, 0.27, and 0.36 m for the x-, y-, and z-axes, respectively. Verification tests were performed on the CSLF in both interior and exterior environments. Based on the spatial characteristics of construction sites, this study identified sound-source locations at construction sites to overcome the limitations of existing sensor-based localization methods. ![]() This study developed a construction sound localization framework (CSLF) using the time delay of the arrival technique and the generalized cross-correlation phase transform. ![]()
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