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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