Abstract As the construction industry experiences a high rate of casualties and significant economic loss associated with accidents, safety has always been a primary concern. In response, several studies have attempted to develop new approaches and state-of-the-art technology for conducting autonomous safety surveillance of construction work zones such as vision-based monitoring. The current and proposed methods including human inspection, however, are limited to consistent and real-time monitoring and rapid event recognition of construction safety issues. In addition, the health and safety risks inherent in construction projects make it challenging for construction workers to be aware of possible safety risks and hazards according to daily planned work activities. To address the urgent demand of the industry to improve worker safety, this study involves the development of an audio-based event detection system to provide daily safety issues to laborers and through the rapid identification of construction accidents. As an evidence-driven approach, the proposed framework incorporates the occupational injury and illness manual data, consisting of historical construction accident data classified by types of sources and events, into an audio-based safety event detection framework. This evidence-driven framework integrated with a daily project schedule can automatically provide construction workers with prenotifications regarding safety hazards at a pertinent work zone as well as consistently contribute to enhanced construction safety monitoring by audio-based event detection. By using a machine learning algorithm, the framework can clearly categorize the narrowed-down sound training data according to a daily project schedule and dynamically restrict sound classification types in advance. The proposed framework is expected to contribute to an emerging knowledge base for integrating an automated safety surveillance system into occupational accident data, significantly improving the accuracy of audio-based event detection.

中文翻译：

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用于预先通知和识别施工安全隐患和事故的证据驱动的声音检测

摘要 建筑行业事故造成的人员伤亡率高、经济损失大，安全一直是人们关注的首要问题。作为回应，一些研究试图开发新的方法和最先进的技术来对建筑工作区进行自主安全监控，例如基于视觉的监控。然而，包括人工检查在内的当前和提议的方法仅限于对施工安全问题的一致和实时监控和快速事件识别。此外，建筑项目固有的健康和安全风险使得建筑工人很难根据日常计划的工作活动了解可能存在的安全风险和危害。为满足业界提高工人安全的迫切需求，这项研究涉及开发基于音频的事件检测系统，以向工人提供日常安全问题，并通过快速识别施工事故。作为一种循证方法，所提议的框架将职业伤害和疾病手册数据（由按来源和事件类型分类的历史建筑事故数据）纳入基于音频的安全事件检测框架。这种与每日项目时间表相结合的循证驱动框架可以自动向建筑工人提供有关相关工作区安全隐患的预先通知，并通过基于音频的事件检测始终有助于加强施工安全监控。通过使用机器学习算法，该框架可以根据每天的项目进度对缩小范围的声音训练数据进行清晰的分类，并预先动态限制声音分类类型。拟议的框架有望为将自动化安全监视系统集成到职业事故数据中的新兴知识库做出贡献，从而显着提高基于音频的事件检测的准确性。