## 1 Introduction  
Increasing personnel and equipment are deployed at construction sites as construction projects have recently become larger and more complex. Consequently, countermeasures and prevention are emerging as important challenges in construction sites due to safety accidents. Rapid response and control systems are being established in many countries, including South Korea, through the establishment of national disaster management institutions. Despite these efforts, approximately 500 people die every year from industrial accidents in South Korea, and the mortality rate per 10,000 workers was 1.47, which is approximately 45% higher than the average mortality rate from industrial accidents of 1.01 (Korea Occupational Safety and Health Agency 2015). Temporary construction, excavation, and reinforced concrete work are the major causes of accidents. An investigation regarding the details of collapse and destruction accidents revealed that most of them were related to collapses during concrete pouring or due to upper loading. Many accidents involving temporary construction, excavation, and reinforced concrete construction work, such as collapse and destruction accidents, were caused by the failure of the formwork and shores to withstand the upper load during concrete pouring.  
Collapse of the formwork or concrete leaks during the pouring process can significantly affect the quality of the structure and cause human and physical damage [1]. Changes inside and outside must be detected and monitored [2] during concrete pouring to prevent the aforementioned accidents. Cameras [3] and several measuring sensors [4] are being introduced, as it is extremely dangerous to monitor directly under the formwork with the naked eye during the pouring work [5]. However, CCTV [6] has limitations in that only a limited range can be covered, and the measurement also has low economic feasibility because it must be attached to the main formwork. It is therefore necessary to establish a monitoring system that can fulfil a wide range of demands. The present study proposes a method that uses sound data [7] to overcome the limitations of sensor- and visual-data-based technologies. Sound data complements the blind spots of a camera, and unlike high-resolution image data, the size of sound data is only one-thousandth that of video data, thus enabling real-time data transmission at construction sites, even at low transmission speeds. Therefore, our aim is to establish a framework that serves as the basis of a system that can react to disasters proactively by identifying unstable conditions and behaviors.  
Recently, algorithms are being developed in order to classify equipment sounds so as to monitor the status of civil engineering sites [8]. While equipment sounds are no longer considered noise which should block sound data in the construction field, they are considered necessary data for management. In previous studies, the primary focus was on collecting and classifying mechanical equipment and work sounds at construction sites. Data that can cause problems with the work status were not collected, but only normal sounds were collected. However, the present study focuses on concrete pouring work, and collected sound data which could affect material separation and formwork collapse through actual experiments, enabling a more systematic understanding of the current status by distinguishing such sounds from normal concrete pouring sounds. A system that can immediately inform safety accidents that may occur during concrete pouring or detect abnormal signs in the early stages of an accident by utilizing sound analysis is developed in this study. The aim is to improve accuracy through deep-learning algorithms based on sound data collected at an actual construction site and to use the data as an effective monitoring method for sound-based work status and safety management in the future.