Neural Networks in the Construction Industry: Knowledge Gaps and Possibilities
Publication: Construction Research Congress 2022
ABSTRACT
Neural Networks (NNs) are becoming more widely used in construction research. They have shown potential when working with prediction and detection algorithms. As found in several papers, NNs outperformed other algorithms in the area of machine learning and have recently improved their applicability, that is, using GANs. The types of NNs are used in construction to solve problems such as recognition of heavy machinery, workers’ pose assessment, and unit price estimations. They range from NNs with one fully connected layer to CNNs, RNNs, and LSTMs with many hidden layers. To understand the future potential of NNs, we completed a bibliometric analysis of the existing literature including publications from journals since 2010. The areas in which NNs have been used in construction applications are defined and mapped to understand the connections between them. The analysis also included a review of the NN model architecture that was used to solve the problems. This work found a wider field of applications for NNs in the construction industry than originally known. New architecture such as transformer networks has not yet been used in construction research but could lead to higher performance networks. A path forward is presented and discussed to realize this potential.
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REFERENCES
Antwi-Afari, M. F., H. Li, E. A. Pärn, and D. J. Edwards. 2018. “Critical success factors for implementing building information modelling (BIM): A longitudinal review.” Autom. Constr. https://doi.org/10.1016/j.autcon.2018.03.010.
Baalousha, Y., and T. Çelik. 2011. “An integrated web-based data warehouse and artificial neural networks system for unit price analysis with inflation adjustment.” J. Civ. Eng. Manage. https://doi.org/10.3846/13923730.2011.576806.
Braun, A., S. Tuttas, A. Borrmann, and U. Stilla. 2020. “Improving progress monitoring by fusing point clouds, semantic data and computer vision.” Autom. Constr. https://doi.org/10.1016/j.autcon.2020.103210.
Cai, J., Y. Zhang, L. Yang, H. Cai, and S. Li. 2020. “A context-augmented deep learning approach for worker trajectory prediction on unstructured and dynamic construction sites” Adv. Eng. Inf. https://doi.org/10.1016/j.aei.2020.101173.
Chou, J., C. Lin, A. Pham, and J. Shao. 2015. “Optimized artificial intelligence models for predicting project award price.” Autom. Constr. https://doi.org/10.1016/j.autcon.2015.02.006.
Cireşan, D. C., U. Meier, L. M. Gambardella, and J. Schmidhuber. 2010. “Deep, big, simple neural nets for handwritten digit recognition.” Neural Comput. https://doi.org/10.1162/NECO_a_00052.
Davis, P., F. Aziz, M. T. Newas, W. Sher, and L. Simon. 2021. “The classification of construction waste material using a deep convolutional neural network.” Autom. Constr. https://doi.org/10.1016/j.autcon.2020.103481.
Ding, L., W. Fang, H. Luo, P. E. D. Love, B. Zhong, and X. Ouyang. 2018. “A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory.” Autom. Constr. https://doi.org/10.1016/j.autcon.2017.11.002.
Fang, Q., H. Li, X. Luo, L. Ding, H. Luo, T. M. Rose, and W. An. 2018a. “Detecting non-hardhat-use by a deep learning method from far-field surveillance videos.” Autom. Constr. https://doi.org/10.1016/j.autcon.2017.09.018.
Fang, Q., H. Li, X. Luo, L. Ding, T. M. Rose, W. An, and Y. Yu. 2018b. “A deep learning-based method for detecting non-certified work on construction sites.” Adv. Eng. Inf. https://doi.org/10.1016/j.aei.2018.01.001.
Fang, W., L. Ding, B. Zhong, P. E. D. Love, and H. Luo. 2018. “Automated detection of workers and heavy equipment on construction sites: A convolutional neural network approach.” Adv. Eng. Inf. https://doi.org/10.1016/j.aei.2018.05.003.
Goodfellow, I., J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Benigo. 2014. “Generative adversarial networks.”.
Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep Learning. MIT Press.
Guo, Y., Y. Xu, and S. Li. 2020. “Dense construction vehicle detection based on orientation-aware feature fusion convolutional neural network.” Autom. Constr. https://doi.org/10.1016/j.autcon.2020.103124.
Hola, B., and K. Schabowicz. 2010. “Estimation of earthworks execution time cost by means of artificial neural networks.” Autom. Constr. https://doi.org/10.1016/j.autcon.2010.02.004.
Hoonyong, L., K. Yang, N. Kim, and C. R. Ahn. 2020. “Detecting excessive load-carrying tasks using a deep learning network with a Gramian Angular Field.” Autom. Constr. https://doi.org/10.1016/j.autcon.2020.103390.
Hyari, K. H., A. Al-Daraiseh, and M. El-Mashaleh. 2016. “Conceptual cost estimation model for engineering services in public construction projects.” J. Manage. Eng. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000381.
Jung, M., and S. Chi. 2020. “Human activity classification based on sound recognition and residual convolutional neural network.” Autom. Constr. https://doi.org/10.1016/j.autcon.2020.103177.
Kim, D., S. Lee, and V. R. Kamat. 2020. “Proximity prediction of mobile objects to prevent contact-driven accidents in co-robotic construction.” J. Comput. Civ. Eng. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000899.
Kim, K., and Y. K. Cho. 2020. “Effective inertial sensor quantity and locations on a body for deep learning-based worker’s motion recognition.” Autom. Constr. https://doi.org/10.1016/j.autcon.2020.103126.
Kim, M., Q. Wang, and H. Li. 2019. “Non-contact sensing based geometric quality assessment of buildings and civil structures: A review.” Autom. Constr. https://doi.org/10.1016/j.autcon.2019.01.002.
Kim, Y., C. H. P. Nguyen, and Y. Choi. 2020. “Automatic pipe and elbow recognition from three-dimensional point cloud model of industrial plant piping system using convolutional neural network-based primitive classification” Autom. Constr. https://doi.org/10.1016/j.autcon.2020.103236.
Kolar, Z., H. Chen, and X. Luo. 2018. “Transfer learning and deep convolutional neural networks for safety guardrail detection in 2D images.” Autom. Constr. https://doi.org/10.1016/j.autcon.2018.01.003.
Krizhevsky, A., I. Sutskever, and G. E. Hinton. 2012. “Imagenet classification with deep convolutional neural networks.” Adv. in Neural Inf. Proc. systems 25 https://doi.org/10.1145/3065386.
Luo, H., C. Xiong, W. Fang, P. E. D. Love, B. Zhang, and X. Ouyang. 2018b. “Convolutional neural networks: Computer vision-based workforce activity assessment in construction.” Autom. Constr. https://doi.org/10.1016/j.autcon.2018.06.007.
Luo, H., M. Wang, P. K. Wong, and J. C. P. Cheng. 2020. “Full body pose estimation of construction equipment using computer vision and deep learning techniques.” Autom. Constr. https://doi.org/10.1016/j.autcon.2019.103016.
Luo, X., H. Li, D. Cao, and F. Dai. 2018a. “Recognizing diverse construction activities in site images via relevance networks of construction-related objects detected by convolutional neural networks.” J. Comput. Civ. Eng. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000756.
Nath, H. D., A. H. Behzadan, and S. G. Paal. 2020. “Deep learning for site safety: Real-time detection of personal protective equipment.” Autom. Constr. https://doi.org/10.1016/j.autcon.2020.103085.
Rashid, K. M., and J. Louis. 2019. “Times-series data augmentation and deep learning for construction equipment activity recognition.” Autom. Constr. https://doi.org/10.1016/j.aei.2019.100944.
Schmidhuber, J. 2015. “Deep learning in neural networks: An overview.” Neural Networks https://doi.org/10.1016/j.neunet.2014.09.003.
Shiha, A., E. M. Dorra, and K. Nassar. 2020. “Neural networks model for prediction of construction material prices in Egypt using macroeconomic indicators.” J. Civ. Eng. Manage. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001785.
Slaton, T., C. Hernandez, and R. Akhavian. 2020. “Construction activity recognition with convolutional recurrent networks.” Autom. Constr. https://doi.org/10.1016/j.autcon.2020.103138.
Son, H., H. Choi, H. Seong, and C. Kim. 2019. “Detection of construction workers under varying poses and changing background in image sequences via very deep residual networks.” Autom. Constr. https://doi.org/10.1016/j.autcon.2018.11.033.
Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin. 2017. “Attention is all you need.”.
Wang, H., and B. Raj. 2017. “On the origin of deep learning.”.
Wang, Z., H. Li, and X. Zhang. 2019. “Construction waste recycling robot for nails and screws: Computer vision technology and neural network approach.” Autom. Constr. https://doi.org/10.1016/j.autcon.2018.11.009.
Wei, R., P. E. D. Love, W. Fang, H. Luo, and S. Xu. 2019. ” Recognizing people’s identity in construction sites with computer vision: A spatial and temporal attention pooling network.” Adv. Eng. Inf. https://doi.org/10.1016/j.aei.2019.100981.
Xiao, B., and S. Kang. 2021. “Vision-based method integrating deep learning detection for tracking multiple construction machines.” J. Comput. Civ. Eng. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000957.
Zhang, H., X. Yan, and H. Li. 2018. “Ergonomic posture recognition using 3D view-invariant features from single ordinary camera.” Autom. Constr. https://doi.org/10.1016/j.autcon.2018.05.033.
Zhang, M., M. Zhu, and X. Zhao. 2020. “Recognition of high-risk scenarios in building construction based on image semantics.” J. Comput. Civ. Eng. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000900.
Zhao, J., and E. Obonyo. 2020. “Convolutional long short-term memory model for recognizing construction workers’ postures from wearable inertial measurement units.” Adv. Eng. Inf. https://doi.org/10.1016/j.aei.2020.101177.
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Construction Research Congress 2022
Pages: 1174 - 1183
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Published online: Mar 7, 2022
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