Machine learning for conservation of architectural heritage

Abstract

PurposeAccurate documentation of damaged or destroyed historical buildings to protect cultural heritage has been on the agenda of architecture for many years. In that sense, this study uses machine learning (ML) to predict missing/damaged parts of historical buildings within the scope of early ottoman tombs.Design/methodology/approachThis study uses conditional generative adversarial networks (cGANs), a subset of ML to predict missing/damaged parts of historical buildings within the scope of early Ottoman tombs. This paper discusses that using GAN as a ML framework is an efficient method for estimating missing/damaged parts of historical buildings. The study uses the plan drawings of nearly 200 historical buildings, which were prepared one by one as a data set for the ML process.FindingsThe study contributes to the field by (1) generating a mixed methodological framework, (2) validating the effectiveness of the proposed framework in the restitution of historical buildings and (3) assessing the contextual dependency of the generated data. The paper provides insights into how ML can be used in the conservation of architectural heritage. It suggests that using a comprehensive data set in the process can be highly effective in getting successful results. The findings of the research will be a reference for new studies on the conservation of cultural heritage with ML and will make a significant contribution to the literature.Research limitations/implicationsA reliable outcome has been obtained concerning the interpretation of documented data and the generation of missing data at the macro level. The framework is remarkably effective when it comes to the identification and re-generation of missing architectural components like walls, domes, windows, doors, etc. on a macro level without details. On the other hand, the proposed methodological framework is not ready for advanced steps of restitution since every case of architectural heritage is very detailed and unique. Therefore, the proposed framework for re-generation of missing components of heritage buildings is limited by the basic geometrical form which means the architectural details of the mentioned components including ornaments, materials, identification of construction layers, etc. are not covered.Originality/valueThe generic literature as to ML models used in architecture mostly constitutes design exploration and floor plan/urban layout generation. More specific studies in the conservation of architectural heritage by using ML mostly focus on architectural component recognition over 3D point cloud data (1) or superficial damage detection of heritage buildings (2). However, we propose a mixed methodological framework for the interpretation of documented architectural data and the regeneration of missing parts of historical buildings. In addition, the methodology and the results of this paper constitute a guide for further research on ML and consequently contribute to architects in the early phases of restitution.

Machine Learning for Pedestrian-Level Wind Comfort Analysis

“…These methods encompass the generation of design intent data, the integration of ML/AI, artificial neural networks (ANN) and deep learning in architectural design, and sustainable urban management in terms of energy efficiency, energy consumption, and infrastructure connectivity, as well as the utilization of ML as a tool at the intersection of art and architecture [37,41,43]. Moreover, the analysis of 2D and 3D data in generative design and the application of AI and ML in sustainable living spaces, urban policies and landscape design [41,43,44], and architectural plan generation [45], including the integration of ML into architectural education [14,[46][47][48][49][50][51][52] and conservation of architectural heritage [53], are also important fields of research. In addition, ML methods have been utilized to forecast carbon emissions during the design stage, as well as to generate design choices for building design with regard to comfort and performance [49,54,55].…”

Contextualized computations: a multi-objective optimization approach for designing contextually responsive building envelopes

“…If the design is deemed not authentic, the generator learns from its mistake and continues iterating until a novel design is achieved (Goodfellow et al ., 2020). GAN algorithms have a wide range of applications in architecture and urbanism, such as design generation and conservation (Boim et al ., 2022; Karadag, 2023). Although GAN algorithms are incredibly powerful, their effectiveness is limited by the data provided to them.…”

Non-destructive testing research on the surface damage faced by the Shanhaiguan Great Wall based on machine learning

“…The properties of these methods are listed in Table 1. Scholars have carried out damage detection applications by performing image processing on cultural heritage objects or traditional buildings (Foti, 2015;Azarafza et al, 2019;Galantucci and Fatiguso, 2019;Wang et al, 2019;Mishra et al, 2022;Samhouri et al, 2022;Karadag, 2023). For example, the architectural heritage of the city of "Al-Salt in Jordan was detected based on the CNN-VGG16 model (Samhouri et al, 2022).…”