Abstracts – Browse Results
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Abdalazeem, M E, Hassan, H, Asawa, T and Mahmoud, H (2024) Green roofs and thermal comfort: a comparative study of soil layers’ seasonal thermal performance integrated with ventilation in hot climate. Architectural Engineering and Design Management, 20(02), 358–89.
Altay, B and Salcı, E (2024) Exploring designers’ finishing materials selection for residential interior spaces. Architectural Engineering and Design Management, 20(02), 269–86.
Aslay, S E and Dede, T (2024) Reduce the construction cost of a 7-story RC public building with metaheuristic algorithms. Architectural Engineering and Design Management, 20(02), 214–29.
EL-Mahdy, D and Ali, M (2024) Assessing the solar radiation performance of self-shaded 3D-printed clay-based façades. Architectural Engineering and Design Management, 20(02), 249–68.
Fan, C (2024) Using convolutional neural networks to identify illegal roofs from unmanned aerial vehicle images. Architectural Engineering and Design Management, 20(02), 390–410.
- Type: Journal Article
- Keywords: Convolutional neural network; illegal roof; unmanned aerial vehicle; digital surface model;
- ISBN/ISSN: 1745-2007
- URL: https://doi.org/10.1080/17452007.2023.2244949
- Abstract:
Illegal structures, which are structures illegally built on land or buildings, are common in Taiwan. Urban residents frequently inform authorities of illegal roofs, a type of illegal structure, because of the potential fire hazards they pose. However, the government does not conduct timely inspection on illegal roofs because this process requires additional human resources. Therefore, developing an efficient and correct method for inspecting and reporting illegal structures is necessary. In this study, unmanned aerial vehicles (UAVs) were used to rapidly capture images, which were then used to generate orthophotos, a 3D building model, a digital surface model (DSM), and a data set containing 400 images of illegal roofs and 400 images of legal roofs. The data set was then used in a convolutional neural network (CNN) to train and evaluate image classification. The results revealed an illegal roof classification accuracy of 96.0%, with a loss of 0.09. In addition, You Only Look Once v3 (YOLOv3) was used to detect illegal buildings, and DSMs higher than 9 m were overlaid to improve the accuracy of the illegal roof identification model. Overall, the study results can help inspectors build a comprehensive database of illegal roofs, which can serve as a reference for budgeting demolition costs and human resources.
Gokyigit Arpaci, E Y, Eksi Akbulut, D and Yildiz, O (2024) Enhancing water resistance of earthen buildings by using admixture materials. Architectural Engineering and Design Management, 20(02), 320–36.
Pérez-Valcárcel, J, Aragón, J, Muñiz, S, Freire-Tellado, M and Mosquera, E (2024) Transportable temporary homes with folding roof. Architectural Engineering and Design Management, 20(02), 337–57.
Ren, S, Qiang, G, Tang, S, Zhang, C, Seo, H and Wu, K (2024) An automatic design-feedback process for structural prefabricated components quantity take-off calculation using BIM. Architectural Engineering and Design Management, 20(02), 287–302.
Sohani, H, Hosseini Nourzad, S H and Saghatforoush, E (2024) The optimized form of building made from the reused elements. Architectural Engineering and Design Management, 20(02), 191–213.
Wang, C, Gao, F, Cui, B, Huang, M M, Wu, M, Mao, L and Zheng, A (2024) Geometric quality assessment of precast concrete (PC) elements based on 3D structural light scanning. Architectural Engineering and Design Management, 20(02), 303–19.
Yuan, Z, Wang, H, Yang, Y, Yi, C, Huang, D and Yu, D (2024) Improving the construction accuracy of precast components in prefabricated buildings by analyzing relevant factors from the perspective of supply chain: a system dynamics model. Architectural Engineering and Design Management, 20(02), 230–48.