Trajectory Prediction of Mobile Construction Resources Toward Pro-active Struck-by Hazard Detection
Trajectory Prediction of Mobile Construction Resources Toward Pro-active Struck-by Hazard Detection
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DOI：
10.22260/isarc2019/0131
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
Daeho Kim;Meiyin Liu;SangHyun Lee;V. Kamat
中科院分区：
文献类型：
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作者：
Daeho Kim;Meiyin Liu;SangHyun Lee;V. Kamat
- 在建筑工程中，经常会发生不可预见的碰撞事故，导致大量的建筑事故。为了解决这个问题，许多研究试图使用各种技术，如无线传感器和计算机视觉方法来自动化接近监测和撞击危险检测。虽然这项技术的重点是了解危险发生时发生了什么，但它并不具备检测未来危险的能力。在即将发生的情况下，检测当前的危险可能无法为工人提供足够的时间采取规避行动。为了解决这一挑战，本研究开发了一个移动的施工资源的轨迹预测模型。具体来说，这项研究对一个名为社会生成对抗网络的深度神经网络进行了超参数调整，以开发一个能够预测5秒以上的预测模型。最后，通过对真实的施工数据的测试，验证了模型的预测精度。结果表明，所开发的模型可以达到令人满意的精度：平均位移误差和最终位移误差分别为0.78和1.27米。轨迹预测允许检测未来的危险，这将支持在危险情况下的主动干预。它最终将有助于促进建筑工人更安全的工作环境。
– In construction, unanticipated struck-by hazards often arise, which have resulted in a significant number of construction fatalities. To address this problem, many studies have attempted to automate proximity monitoring and struck-by hazard detection using various technologies, such as wireless sensors and computer vision methods. While this technology focuses on understanding what is happening as hazards arise, it is not equipped to detect future hazards. In impending situations, detecting current hazards may not provide enough time for workers to take evasive actions. To address this challenge this study develops a trajectory prediction model for mobile construction resources. Specifically, this study conducts hyper-parameter tuning of a deep neural network, called Social Generative Adversarial Network to develop a prediction model capable of predicting more than five seconds. Further, a test on a real construction operations data follows to validate developed models’ trajectory prediction accuracy. As a result, a developed model could achieve promising accuracy: the average displacement error and the final displacement error were 0.78 and 1.27 meters, respectively. The trajectory prediction allows for detecting future hazards, which will support pro-active intervention in hazardous situations. It will ultimately contribute to promoting a safer working environment for construction workers.