An Audio Data-Driven Roadway Digital Twin and its Underlying Framework for Digitized Transportation Construction Environment

Deria, Anisha; Chacon, Pedro J.; Lee, Yong-Cheol; Choi, Jin‐Woo

doi:10.2139/ssrn.4149532

Cited by 1 publication

(1 citation statement)

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“…As a result, sound-based methods are well suited for tracking the status and duration of resource construction activities, and they have been used on construction sites for schedule management [15], productivity analysis [16], safety management [17], and other applications. Sound is envisaged to contribute to the construction phase's digital twin [18].…”

Section: Introductionmentioning

confidence: 99%

Sound-Based Construction Activity Monitoring with Deep Learning

Xiong

Xu²,

Chen³

et al. 2022

Buildings

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Automated construction monitoring assists site managers in managing safety, schedule, and productivity effectively. Existing research focuses on identifying construction sounds to determine the type of construction activity. However, there are two major limitations: the inability to handle a mixed sound environment in which multiple construction activity sounds occur simultaneously, and the inability to precisely locate the start and end times of each individual construction activity. This research aims to fill this gap through developing an innovative deep learning-based method. The proposed model combines the benefits of Convolutional Neural Network (CNN) for extracting features and Recurrent Neural Network (RNN) for leveraging contextual information to handle construction environments with polyphony and noise. In addition, the dual threshold output permits exact identification of the start and finish timings of individual construction activities. Before training and testing with construction sounds collected from a modular construction factory, the model has been pre-trained with publicly available general sound event data. All of the innovative designs have been confirmed by an ablation study, and two extended experiments were also performed to verify the versatility of the present model in additional construction environments or activities. This model has great potential to be used for autonomous monitoring of construction activities.

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