Journal of Cultural Heritage最新文献

Cenabi Ahmet Pasha Mosque (Cenab-ı Ahmet Paşa Mosque) is known to be the only work that resembles the style of Architect Sinan in Ankara. In this study, it is aimed to examine the two-dimensional muqarnas scheme of the mosque's main gate and to determine through algorithmic modeling the combination of muqarnas cells in the third dimension. By the photogrammetry method, the grid and star forms used in the muqarnas plan scheme are revealed. The geometric characteristic of the muqarnas in two dimensions is determined. Cell types of the muqarnas and their geometric configurations are obtained. It is demonstrated that there is a radial grid in the plan of the Cenabi Ahmet Pasha Mosque main gate muqarnas, and that the three-dimensional structure of the muqarnas consisting of six layers includes almond cell, triangle cell, deltoid cell, intermediate triangle cell, intermediate deltoid cell, biped cell and triped cell. Additionally, an algorithmic model based on the creation of cell unit system is proposed for the creation of the muqarnas geometry in 3D.

Thangka, a masterpiece of Tibetan painting, is renowned for its adept use of natural mineral pigments such as gold, turquoise, and cinnabar, which imbue it with profound artistic and historical significance. Presently, chemical analysis methods relying on microscopic perspectives are prevalent in researching the pigment components of cultural artifacts. However, these methods suffer from quantization gaps and carry the risk of damaging the relics. Hence, in this study, we focus on Thangka Five Buddha as our experimental sample and propose a novel approach utilizing Linear Spectral Mixed Analysis (LSMA) based on Hyperspectral Imaging (HSI) technology to perform quantitative analysis of pigment components at a sub-pixel level. The results indicate the following. 1) A database of spectral curves for 25 representative Thangka pigments was established, covering 196 bands from 393 to 800 nm VIR-NIR range. 2) The LSMA model successfully separated the 13 pigment components of the Thangka at the sub-pixel level, achieving a Root Mean Square Error (RMSE) of 0.0186, which indicates high classification accuracy. 3) The quantitative analysis reveals that 33.07 % of the area is painted using a single pigment, while 56.01 % is painted using a combination of two pigments. Verdigris (18.56 %), malachite (17.52 %), and cinnabar (10.91 %) are the pigment types with the highest proportions among them. Out of the 521 pigment combinations, verdigris and turquoise (4.55 %), malachite and calcite (4.02 %), minium and cinnabar (2.87 %), and turquoise and malachite (2.82 %) are more commonly used. 4) The application of quantitative analysis methods demonstrates significant potential in painting techniques, authentication processes, and establishing historical dating, among other areas of study.

Ancient mural degradation and destruction may result from various natural causes, resulting in cracks, peeling, or bulging. As such, regular testing and evaluation of ancient murals are indispensable for protecting and preserving cultural relics. In many scenarios, the acquisition of detection data can be expedited through the use of mechanical arms and imaging equipment. However, the subsequent data analysis relies on experienced human inspectors, resulting in a laborious and time-consuming process. This study focuses on automated analysis of cracks in ancient murals using optical pulsed thermography. A technique that combines an attention mechanism and the U-Net neural network is proposed for refined crack feature extraction. Concerning the identification of ancient mural cracks based on limited training data, U-Net with the attention mechanism demonstrates superior performance over both the conventional U-Net and a traditional image segmentation algorithm.

Cultural tourism has become an increasingly growing human activity in today's world. Therefore, various cultural/natural heritage areas or buildings face large tourist crowds, causing physical, social, or cultural deterioration. Cultural routes, which provide a systematic approach and planned tourists move, can reduce the negative effects of overtourism. This study aims to develop an optimization model as a supportive tool for the cultural route design process. The proposed optimization model aims to maximize cultural experience while satisfying constraints on heritage values, building types, historical eras, and general routing rules. Moreover, this model, creating optimized cultural routes, is expected to contribute to regulating economic income and tourist density. As a case study, the ancient water supply systems of Istanbul, inherited from the Roman and the Ottoman empires and currently overlooked, are investigated. Heritage values, mentioned in the extensive literature, are reviewed and the values/attributes for this study are appointed according to the characteristics of the case heritage assets, and their environment. The values of the case structures are evaluated with value analysis after field study. An optimum cultural route for Istanbul's historical water supply heritage, according to the objective and the constraints of the model, is obtained by solving the model with the case data. The resulting route can make this overlooked heritage of Istanbul recognized and spread the tourist density in the city center. The model is flexible, allowing for easy modification of the objective function, constraints, and values when designing routes across various heritage sites. This model can be used not only in the design of new cultural routes but also in optimizing existing routes.

The study created an electrospun fiber membrane with polyacrylonitrile, nano silver, and oregano oil using the electrospinning technique. After examining the preparation parameters, an appropriate formula for the composite film spinning solution and electrospinning parameters were found: 11 wt percent polyacrylonitrile, 6 wt percent natural oregano essential oil, 15 kV of spinning voltage, 0.6 mL/h of advancing rate, and 20 cm of spinning distance. The composite nanofiber membrane demonstrated improved shape and structure, a more balanced antibacterial effect, and persistence against microbes when subjected to the prescribed formula and circumstances. In the end, the strong antimicrobial qualities of the nano-fiber material were validated by the Feilaifeng field testing. This study showed that when combined with natural plant essential oil, the composite nanofiber membrane has potential for use as a green microbial control material for Grottoes historic heritage. By preventing an intense direct contact between antimirobial agents and the cultural relics of the grottoes, the composite barrier reduced the potential harm that could be caused by the solution soaking into the rocks. Moreover, the composite nanofiber membrane provides the advantages of large-scale antibacterial activity from natural essential oil and long-lasting antimicrobial capabilities from nanosilver. It also has no effect on the cultural relics of the Grotto, is simple to remove, and leaves little trace.

Built cultural heritage is exposed to various deterioration problems caused by different types of actions. To reduce the need for major interventions, preventive conservation (PC) approaches were proposed, based on data collection, regular monitoring, inspections, and control of environmental factors. Monitoring actions able to depict the evolution of buildings’ deterioration state, have been proposed and implemented in real cases. Considering that digital images (DI) of historical facades are constantly collected by different subjects and for different purposes, they represent the widest existing data source to support PC approaches and develop predictive tools. DI of historical façades can be used to help in the early recognition of different types of deterioration processes, supporting the creation and application of predictive models based on machine learning (ML) methods. This work proposes a method for the automatic detection of biological colonisation of building facades. A convolutional neural network (CNN) has been trained and tested with images representing the microalgae and cyanobacteria growth process on historical bricks’ facades, collected during experimental activities in controlled conditions. The trained model is characterized by an accuracy of 87 % and can recognise bio-colonisation on different types of bricks. The trained model has been applied to a historical building used as a case study. The facades of the case study are constantly monitored by surveillance cameras, and DI of the facades are often collected due to the public function of the building. The study shows that by simply processing these images with the trained network it is possible to detect the first stage of bio-deterioration processes. This work is part of more extensive research for the early detection of different types of building façade damages and can be easily implemented where DI coming from surveillance cameras or other sources are available.

In recent years, machine learning (ML) has gained significant importance in the field of cultural heritage research. Its advanced data analysis techniques have become a crucial tool in many areas of heritage science. This literature review intends to discuss the applications of ML to studies on cultural heritage objects using the analytical chemistry methods. The analysis of large datasets obtained from complex measurements with the use of ML algorithms has been demonstrated to result in a deeper understanding of the studied objects. Such analyses have also been shown to provide new perspectives on many problems. The article outlines studies on varied materials such as pigments, paper, metals, and ceramics. It presents analyses that use diverse ML methods, including unsupervised and supervised techniques, utilizing both traditional algorithms and neural networks. It also provides an introduction to understanding ML, its principles and methods, with the focus on practices applicable to heritage science.