Various mosque designs appear in Indonesia, with various shapes, with or without domes. Until now the vis-ualization of mosque formation that mosque has domes still accepted in general. Architectural forms are part of the visual language that is bound by the rules of geometry and proportion. Historically, the proportion of the Golden Section is a mathematical comparison applied to the design process. While in design, architects often involve a sense, whether the building design has been proportional or not. The research question, whether the sense of a proportional form can be measured mathematically? The results show that Golden Section elements exist in some parts of the mosque. The research explores the design of 3 mosques, At Tin Mosque in Taman Mini Indonesia Indah, Dian Al Mahri Mosque in Depok, and Trans Studio's Grand Mosque in Bandung, private built mosques, with geometric analysis methods and visual observations. The research benefits to explore the golden section are to control the building composition.
This paper addresses the general problem of built heritage protection against both deterioration and loss. In order to continuously monitor and update the structural health status, a crowd-sensing solution based on powerful and automatic deep learning technique is proposed. The aim of this solution is to get rid of the limitations of manual and visual damage detection methods that are costly and time consuming. Instead, automatic visual inspection for damage detection on walls is efficiently and effectively performed using an embedded Convolutional Neural Network (CNN). This CNN detects the most frequent types of surface damage on wall photos. The study has been conducted in the Kasbah of Algiers where the four following types of damages have been considered: Efflorescence, spall, crack, and mold. The CNN is designed and trained to be integrated into a mobile application for a participatory crowd-sensing solution. The application should be widely and freely deployed, so that any user can take a picture of a suspected damaged wall, and get an instant and automatic diagnosis, through the embedded CNN. In this context, we have chosen MobileNetV2 with a transfer learning approach. A set of real images have been collected and manually annotated, and have been used for training, validation, and test. Extensive experiments have been conducted in order to assess the efficiency and the effectiveness of the proposed solution, using a 5 fold cross validation procedure. Obtained results show in particular a mean weighted average precision of 0.868 ± 0.00862 (with a 99% of confidence level) and a mean weighted average recall of 0.84 ± 0.00729 (with a 99% of confidence level). To evaluate the performance of MobileNetV2 as a feature extractor, we conducted a comparative study with other small backbones. Further analysis of CNN activation using Grad-Cam has also been done. Obtained results show that our method remains effective even when using a small network and medium to low resolution images. MobileNetV2-based CNN size is smaller, and computational cost better, compared to the other CNNs, with similar performance results. Finally, detected surface damages have also been plotted on a geographic map, giving a global view of their distribution.
Style in Islamic architecture is generally characterized as common features appearing in a class of buildings. This research considers style as an ordering principle. It proposes new tools for style analysis drawn from modern mathematics. A morphological approach is applied to four façades of North African Medersas buildings constructed between the fourteenth and sixteenth centuries. The results show that these facades share compositional similarities, and that their order and composition are governed by topological relationships. The application of graph theory and the use of computing tools make it possible to objectify the understanding of the style of the façade, and demonstrates how the representation of the façade in the form of a graph can reveal the structural topological arrangement as well as the ability to conduct calculations on the graph of the façade. The betweenness centrality measure reveals the existence of different interconnected levels of hierarchy, controlling the system of the facade.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.