Advanced Sound Classifiers and Performance Analyses for Accurate Audio-Based Construction Project Monitoring
01 Sep 2020-Journal of Computing in Civil Engineering (American Society of Civil Engineers)-Vol. 34, Iss: 5, pp 04020030
TL;DR: The sounds of work activities and equipment operations at a construction site provide critical information regarding construction progress, task performance, and safety issues.
Abstract: The sounds of work activities and equipment operations at a construction site provide critical information regarding construction progress, task performance, and safety issues The construc
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27 Nov 2020
TL;DR: DTC should be viewed as a comprehensive mode of construction that prioritizes closing the control loops rather than an extension of BIM tools integrated with sensing and monitoring technologies.
Abstract: The concept of a “digital twin” as a model for data-driven management and control of physical systems has emerged over the past decade in the domains of manufacturing, production, and operations. In the context of buildings and civil infrastructure, the notion of a digital twin remains ill-defined, with little or no consensus among researchers and practitioners of the ways in which digital twin processes and data-centric technologies can support design and construction. This paper builds on existing concepts of Building Information Modeling (BIM), lean project production systems, automated data acquisition from construction sites and supply chains, and artificial intelligence to formulate a mode of construction that applies digital twin information systems to achieve closed loop control systems. It contributes a set of four core information and control concepts for digital twin construction (DTC), which define the dimensions of the conceptual space for the information used in DTC workflows. Working from the core concepts, we propose a DTC information system workflow—including information stores, information processing functions, and monitoring technologies—according to three concentric control workflow cycles. DTC should be viewed as a comprehensive mode of construction that prioritizes closing the control loops rather than an extension of BIM tools integrated with sensing and monitoring technologies.
162 citations
TL;DR: This research proposes a novel machine learning method based on the integration of Random Forest classifier with the fractional calculus-based feature augmentation technique to develop an accurate activity recognition model using a limited dataset.
32 citations
TL;DR: A Machine Learning methodology was developed to train and evaluate 13 classifiers using artificial features extracted from raw accelerometer data segments, indicating that accelerometers can be used to create a robust system to recognise large sets of Construction worker activities automatically.
Abstract: Automated Construction worker activity classification has the potential to not only benefit the worker performance in terms of productivity and safety, but also the overall project management and control. The activity-level knowledge and indicators that can be extracted from this process may support project decision making, aiding in project schedule adjustment, resource management, construction site control, among others. Previous works on this topic focused on the collection and classification of worker acceleration data using wearable accelerometers and supervised machine learning algorithms, respectively. However, most of these studies tend to consider small sets of activities performed in an instructed manner, which can lead to higher accuracy results than those expected in a real construction scenario. To this end, this paper builds on the results of these past studies, committing to expand this discussion by covering a larger set of complex Construction activities than the current state-of-the-art, while avoiding the need to instruct test subjects on how and when to perform each activity. As such, a Machine Learning methodology was developed to train and evaluate 13 classifiers using artificial features extracted from raw accelerometer data segments. An experimental study was carried out under the form of a realistic activity-circuit to recognise ten different activities: gearing up; hammering; masonry; painting; roughcasting; sawing; screwing; sitting; standing still; and walking; with most activities being a cluster of simpler tasks (i.e. masonry includes fetching, transporting, and laying bricks). Activities were initially separated and tested in three different activity groups, before assessing all activities together. It was found that a segment length of 6 s, with a 75% overlap, enhanced the classifier performance. Feature selection was carried out to speed the algorithm running time. A nested cross-validation approach was performed for hyperparameter tuning and classifier training and testing. User-dependent and -independent approaches (differing in whether the system must undergo an additional training phase for each new user) were evaluated. Results indicate that accelerometers can be used to create a robust system to recognise large sets of Construction worker activities automatically. The K-Nearest Neighbours and Gradient Boosting algorithms were selected according to their performances, respectively, for the user-dependent and -independent scenarios. In both cases, the classifiers showed balanced accuracies above 84% for their respective approaches and test groups. Results also indicate that a user-dependent approach using task groups provides the highest accuracy.
27 citations
TL;DR: The aim of the work is to obtain an accurate and flexible tool for consistently executing and managing the unmanned monitoring of construction sites by using distributed acoustic sensors by using a Deep Belief Network based approach.
Abstract: In this paper, we propose a Deep Belief Network (DBN) based approach for the classification of audio signals to improve work activity identification and remote surveillance of construction projects. The aim of the work is to obtain an accurate and flexible tool for consistently executing and managing the unmanned monitoring of construction sites by using distributed acoustic sensors. In this paper, ten classes of multiple construction equipment and tools, frequently and broadly used in construction sites, have been collected and examined to conduct and validate the proposed approach. The input provided to the DBN consists in the concatenation of several statistics evaluated by a set of spectral features, like MFCCs and mel-scaled spectrogram. The proposed architecture, along with the preprocessing and the feature extraction steps, has been described in details while the effectiveness of the proposed idea has been demonstrated by some numerical results, evaluated by using real-world recordings. The final overall accuracy on the test set is up to 98% and is a significantly improved performance compared to other state-of-the-are approaches. A practical and real-time application of the presented method has been also proposed in order to apply the classification scheme to sound data recorded in different environmental scenarios.
23 citations
01 Jan 2021
TL;DR: This work developed an application for the classification of different types and brands of construction vehicles and tools, which operates on the emitted audio through a stack of convolutional layers, demonstrating its effectiveness in environmental sound classification (ESC) achieving a high accuracy.
Abstract: Convolutional Neural Networks (CNNs) have been widely used in the field of audio recognition and classification, since they often provide positive results. Motivated by the success of this kind of approach and the lack of practical methodologies for the monitoring of construction sites by using audio data, we developed an application for the classification of different types and brands of construction vehicles and tools, which operates on the emitted audio through a stack of convolutional layers. The proposed architecture works on the mel-spectrogram representation of the input audio frames and it demonstrates its effectiveness in environmental sound classification (ESC) achieving a high accuracy. In summary, our contribution shows that techniques employed for general ESC can be also successfully adapted to a more specific environmental sound classification task, such as event recognition in construction sites.
18 citations
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