Preliminary Analysis of Emerging Visualization and Image Modeling Technologies for Highway Construction Inspection

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Abstract

Visualization and image modeling technologies have been proven as an efficient tool for improving inspection of transportation projects in the last two decades due to their capabilities in collecting field data and documenting inspection results. Different visualization and image modeling techniques have been developed and implemented in the current construction inspection literature. A gap exists regarding a synthesis to summarize and assess the current practices of these techniques in construction inspection activities. The objectives of this study are to (1) develop a list of emerging visualization and image modeling technologies along with the inspected elements of construction projects and (2) highlight the mainstream of the current practices along with predictions of future research directions. A systematic literature review and content analysis of 29 selected articles from 11 renowned journals in construction management published during 2000–2021 was conducted. The results of profiling 14 technologies showed that merging two or more of the visualization and image modeling technologies has a promising potential to overcome limitations of some of these technologies. A considerable number of technologies such as dual-light inspection and Electro-optical imagery are still under evaluation and have had limited deployment in construction inspection. In addition, the finding showed that managing and transferring data among different e-inspection technologies are main challenging in implementation of construction inspection technologies. This study is of value to researchers and industry practitioners seeking a useful reference on adopting new technologies in construction inspection.

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Mohamed, M., Nguyen, P., & Tran, D. (2023). Preliminary Analysis of Emerging Visualization and Image Modeling Technologies for Highway Construction Inspection. In Lecture Notes in Civil Engineering (Vol. 247, pp. 433–445). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-19-0968-9_35

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