Search Results (4)

As a result of environmental and human influences, several types of surface deterioration emerge on historic buildings, resulting in a decline in the quality of these structures and even threats to their safety. In the conventional approach, assessing the surface damage on a structure involves the time-consuming and labor-intensive judgment and evaluation of trained professionals. In this study, it is suggested that the YOLOv4 machine learning model be used to automatically find five types of damage to historical gray-brick buildings. This would make the job go more quickly. This study uses the gray-brick wall buildings in the buffer zone of the global cultural heritage in Macau as an example. In total, 1355 photographs were taken on-site of the gray-brick walls, and the five most common types of damage were identified. By slicing and labeling the photos, a training set of 1000 images was created, and through 200-generation model training, the model can accurately identify and effectively identify the damage state of the gray bricks and enhance the quality judgment and evaluation of the exterior walls of historical buildings. Experiments allow us to reach the following conclusions: (1) The damage to the gray-brick ancient buildings in Macau is affected by the subtropical maritime climate. Missing paint, stains, and cracks are the main contributors to gray-brick wall damage. (2) Machine learning can help determine the type of damage to old gray-brick buildings, which is useful for managing and protecting historical buildings. (3) The model in this study can identify five types of damage: missing, cracking, plant or microbial erosion, yellowing, and pollution on the exterior walls of ancient gray-brick buildings. It is helpful to accurately identify and evaluate the damaged condition of the gray-brick wall and formulate corresponding protection schemes. Full article

Most methods for repairing damaged old photos are manual or semi-automatic. With these methods, the damaged region must first be manually marked so that it can be repaired later either by hand or by an algorithm. However, damage marking is a time-consuming and labor-intensive process. Although there are a few fully automatic repair methods, they are in the style of end-to-end repairing, which means they provide no control over damaged area detection, potentially destroying or being unable to completely preserve valuable historical photos to the full degree. Therefore, this paper proposes a deep learning-based architecture for automatically detecting damaged areas of old photos. We designed a damage detection model to automatically and correctly mark damaged areas in photos, and this damage can be subsequently repaired using any existing inpainting methods. Our experimental results show that our proposed damage detection model can detect complex damaged areas in old photos automatically and effectively. The damage marking time is substantially reduced to less than 0.01 s per photo to speed up old photo recovery processing. Full article

The “Tianzihao” colony was built by the French Jesuits in the 1890s. As one of the earliest examples of the French Catholic Church’s mission in China, as well as the only case in Nanjing, it shows the historical scenes of Western missionaries in Nanjing 120 years ago. It is a demonstration of cultural exchanges between China and the West after China opened to the Western world in the late 19th century. In architectural style, the “Tianzihao” colony is Western-style townhouses, but a large number of traditional Chinese architectural technologies were used for it, and therefore it is characterized by Western space and Chinese technology. The “Tianzihao” colony was badly damaged during these decades, with a lot of decayed building materials and structures on the verge of collapse. Based on the historical research and technical analysis of the “Tianzihao” colony, this article explores the conservation strategies and methods of reusing the architectural heritage. In addition, this article is to study the characteristics of the times before introduction of Western architectural technology in Nanjing based on an analysis on the building technology used for the “Tianzihao” colony. The authors participated in the conservation and restoration project of the “Tianzihao” colony, and the objective of this study was achieved through some qualitative methods, including collection and analysis of archival data, analysis of old maps and photos, architectural mapping and a large amount of historical information found in the conservation process. Full article

Traumatic brain injury (TBI), also known as intracranial injury, occurs when an external force injures the brain. This study aimed to analyze the factors affecting the presence of TBI in the elderly occupants of motor vehicle crashes. We defined elderly occupants as those more than 55 years old. Damage to the vehicle was presented using the Collision Deformation Classification (CDC) code by evaluation of photos of the damaged vehicle, and a trauma score was used for evaluation of the severity of the patient’s injury. A logistic regression model was used to identify factors affecting TBI in elderly occupants and a predictive model was constructed. We performed this study retrospectively and gathered all the data under the Korean In-Depth Accident Study (KIDAS) investigation system. Among 3697 patients who visited the emergency room in the regional emergency medical center due to motor vehicle crashes from 2011 to 2018, we analyzed the data of 822 elderly occupants, which were divided into two groups: the TBI patients (N = 357) and the non-TBI patients (N = 465). According to multiple logistic regression analysis, the probabilities of TBI in the elderly caused by rear-end (OR = 1.833) and multiple collisions (OR = 1.897) were higher than in frontal collision. Furthermore, the probability of TBI in the elderly was 1.677 times higher in those with unfastened seatbelts compared to those with fastened seatbelts (OR = 1.677). This study was meaningful in that it incorporated several indicators that affected the occurrence of the TBI in the elderly occupants. In addition, it was performed to determine the probability of TBI according to sex, vehicle type, seating position, seatbelt status, collision type, and crush extent using logistic regression analysis. In order to derive more precise predictive models, it would be needed to analyze more factors for vehicle damage, environment, and occupant injury in future studies. Full article