In this paper, Macau Taipa Village, a historical district in an island-type city, is the research focus, and a parametric design-based method for designing historical districts is proposed. First, the shape and distribution of roads in historical districts is studied through on-the-spot investigation and measurement and data are collected on the width, depth, and area of street blocks, average street length and width, street orientation, building width, depth, acreage, and height, and building orientation. Second, the acquired data are transmitted to the Grasshopper parameterization platform, and an intelligently generated program produces calculations. Through multiple iterations and evaluations of genetic algorithms based on the needs of modern urban design and planning constraints, the building density is further reduced, and more landscape areas are added. The parametric model is optimized to gradually generate the urban spatial design that meets contemporary requirements. The research reveals that (1) the grasshopper algorithm’s generated block design inherits the characteristics of the conventional historical block. (2) The historical block where the data were collected on site and the block where Grasshopper is applied should maintain the consistency of the regional context. (3) This method can be applied to the design of new urban areas, and the scope of application can be further adjusted through collaboration and the needs of the owners at a later stage.

The Shanhaiguan Great Wall is a section of the Great Wall of the Ming Dynasty, which is a UNESCO World Heritage Site. Both sides of its basic structure are composed of rammed earth and gray bricks. The surface gray bricks sustain damage from environmental factors, resulting in a decline in their structural quality and even a threat to their safety. Traditional surface damage detection methods rely primarily on manual identification or manual identification following unmanned aerial vehicle (UAV) aerial photography, which is labor-intensive. This paper applies the YOLOv4 machine learning model to the gray surface bricks of the Plain Great Wall of Shanhaiguan as an illustration. By slicing and labeling the photos, creating a training set, and then training the model, the proposed approach automatically detects four types of damage (chalking, plants, ubiquinol, and cracking) on the surface of the Great Wall. This eliminates the need to expend costly human resources for manual identification following aerial photography, thereby accelerating the work. Through research, it is found that (1) compared with manual detection, this method can quickly and efficiently monitor a large number of wall samples in a short period of time and improve the efficiency of brick wall detection in ancient buildings. (2) Compared with previous approaches, the accuracy of the current method is improved. The identifiable types are increased to include chalking and ubiquinol, and the accuracy rate increases by 0.17% (from 85.70% before to 85.87% now). (3) This method can quickly identify the damaged parts of the wall without damaging the appearance of the historical building structure, enabling timely repair measures.

With the rapid growth of global urbanization and the rising demand for sustainable development, it is essential to study the performance and durability of building materials. As a traditional and widely employed building material, Chinese clay tiles play a significant role in traditional Chinese architecture. However, traditional methods for assessing structural surface damage necessitate the time-consuming and labor-intensive assessment and judgment of trained professionals. Consequently, it is crucial to employ machine learning techniques for automatic damage type identification. This study identifies the types of damage to Chinese clay tiles in Macau by employing machine learning techniques and the YOLOv4 object detection model. A total of 363 photographs of on-site Chinese clay tiles were used as training samples, and 200 epochs of the model training were performed. The primary findings of this study are as follows: (1) The machine learning method, based on the YOLOv4 model, provides an effective and precise solution for the automatic identification of Chinese clay tiles damage types, overcoming the human and time-cost constraints of conventional evaluation methods. (2) The detection accuracy of the detection model in this study is 95.42% for the detection of Chinese clay tiles cracks, 80.91% for the detection of stains, and 89.34% for the detection of surface wear, with an overall accuracy of 88.98%, which meets the basic detection requirements. (3) The experimental results demonstrate the viability and efficacy of the proposed method for identifying clay tile damage types and provide a method reference for the preservation and sustainable development of historical buildings.

The COVID-19 epidemic poses a global challenge. Urban wind environments, as integral parts of cities, may contribute to virus spread. Understanding their impact is vital for effective prevention strategies. This study employs a conditional generative confrontation network (CGAN) to train a model using simulated urban wind and COVID-19 distribution data. The model predicts COVID-19 distribution probability under various wind environments, revealing their relationship.

Artificial intelligence (AI) has influenced the design field, offering solutions to urban renewal design problems. This study aims to develop a stylized generation technology for building facade decoration in historic districts using conditional generative adversarial networks (CGANs). The research focuses on Putian, a historic district in Fujian Province. It includes image data acquisition, processing, screening, model training, image generation, and style matching. Findings show CGAN technology effectively generates historical district decorative styles, providing overall or partial facade design schemes. The method adapts to historical district reconstruction, facade renovation, and renovation design, especially for districts with distinct styles. It aids in determining facade decoration styles for specific historical buildings. The method learns internal laws of complex district styles, generating new designs with clear style attributes. It enhances stylized control in historical heritage protection, improving design efficiency.

The COVID-19 pandemic has prompted a reassessment of urban space, including planning and architecture. This study employs a conditional generative adversarial network (CGAN) to derive the distribution of urban texture based on COVID-19 hotspots. It establishes a link between urban form and the pandemic, enabling the machine to predict high-risk urban forms. The study focuses on Macau, conducting model training, image generation, and comparing results for different assumed epidemic distribution degrees. The implications for urban planning are as follows: 1) Urban forms exhibit a correlation with epidemic distribution, allowing CGAN to predict high-risk urban forms; 2) Large-scale and high-density buildings promote COVID-19 transmission; 3) Green public open spaces and squares inhibit COVID-19 spread; and 4) Reducing building volume and density while increasing green public spaces can help mitigate COVID-19 distribution. This research offers valuable insights and potential applications in planning and design.

Machine learning (ML) is used to predict distribution of COVID-19 distribution in urban areas. This study utilizes a conditional generative adversarial network (CGAN) to predict COVID-19 hotspots based on urban texture and business formats. By establishing a relationship between urban elements and COVID-19, machines can automatically predict epidemic hotspots. Using Macau as an example, the method correlates urban texture and business hotspots with new COVID-19 clusters. Different samples yield varying prediction accuracies. Results show: (1) CGAN accurately predicts COVID-19 distribution with over 70% accuracy. (2) Predicting using urban texture and hospital/station POI data achieves over 60% accuracy in Macau. (3) The method also predicts other at-risk areas, aiding urban epidemic control.

The paper proposes a method for designing the floor plans of museum exhibition halls using a conditional generative adversarial network (CGAN). The traditional approach of designing exhibition hall floor plans individually for each floor in a multi-story building is time-consuming and inefficient. The CGAN-based method aims to streamline the design process and help architects work more efficiently.

This study examines the relationship between folk religious place-making and urban public spaces, focusing on Macau’s Shi Gandang Temple. The research utilizes on-site visits, historical evidence, and interviews to analyze place-making processes, social functions, management mechanisms, and folk culture. The findings emphasize the significant influence of folk beliefs on Macau’s urban spaces and social management system. They contribute to the development of local communities and geopolitical relations. The study highlights the participation of folk religious places as key nodes and emotional hubs, facilitating conflict resolution among diverse communities. It also promotes positive interactions, cooperation, and integration of regional development with management mechanisms, governance models, and cultural orientations.

This study examines the wind environment and urban form in Macau’s high-rise residential area of Areia Preta. Areia Preta’s proximity to the tropical ocean, high population density, and tall buildings necessitate effective ventilation and heat dissipation. The research focuses on the relationship between spatial form and the wind environment, particularly in relation to summer typhoons that endanger high-rise structures. By utilizing residential samples. Agglomeration analysis, and the PHOENICS software, the study simulates prevailing monsoon conditions and extreme typhoon scenarios to identify wind characteristics. The findings inform conclusions about urban form and propose strategies to mitigate building-to-building shielding and minimize typhoon damage. This research provides theoretical insights and practical references for urban construction, high-rise building planning, and layout considerations.

This paper takes the garden road system as the research object and proposes a method of generating paths for classical gardens based on parametric design. Firstly, by studying the distribution characteristics of roads, the data on the curvature, angle, and view area of roads were collected. Secondly, the obtained data were transferred to the parameterized platform, and a method of intelligent generation was used for calculation. Finally, the road system was optimized by the genetic algorithm for better application in modern landscape design. According to the current situation, the road system plan generated by the algorithm inherits the characteristics of classical garden roads. This method can be applied to the courtyard, the community park, the urban park, and other objects. This research not only identifies the characteristics of landscape cultural heritage, but also produces an innovative, intelligent design tool. It provides new methods for the parameterized inheritance and application of traditional landscape heritage.

This study proposes using the YOLOv4 machine learning model to automatically detect and classify five types of damage on historic gray-brick buildings, improving efficiency compared to manual assessment. The research focuses on gray- brick wall buildings in Macau’s global cultural heritage buffer zone. A total of 1355 on-site photographs were collected, identifying the five most common types of damage. From these images, a training set of 1000 labeled images was created for model training over 200 generations. The study concludes that gray-brick buildings in Macau sustain damage from the subtropical maritime climate, including missing paint, stains, and cracks. Machine learning aids in identifying damage types, assisting in management and protection. The proposed model accurately detects missing, cracking, erosion, yellowing, and pollution on gray-brick walls, enabling precise evaluation and protection strategies.

This research addresses the challenge of achieving a unified planning of architectural styles in urban expansion and preserving traditional buildings in villages and towns. It uses a conditional generative adversarial network (CGAN) to develop a technique for designing building facades in villages and cities. The results serve as design references, enhancing efficiency. The CGAN-Assisted model is applied to rehabilitate building facades and in visual design for rural tourism products, demonstrating practical usefulness and design potential. Using villages and towns in China’s Wuyishan area as an example, the study involves model training, image generation, and comparing results for different assumed buildings.

景观视线分析是城市设计实践和研究领域的关键之一，尤其是对于城市中的重要景点的可视区域和建筑遮挡的准确控制。 在城市迅猛发展下，历史城镇景观的保护与城市建设高度的开发争议不断。 通过运用参数化软件作为模拟分析平台，建立一种基于视点和景点之间三维连线并扣除建筑物遮挡后所得到的虚拟户外空间人眼纺锤形视线观察方法。 以澳门东望洋山周边区域和南湾湖片区为例，计算得出东望洋山周边的户外空间可视区域，以及主教山小堂和海面眺望视线控制下的南湾湖片区建筑形态和高度。 计算结果表明，参数化景观视线分析方法实现了量化、高效、可视化的目的，后续有利于指导城市景观风貌的保护和规划设计。

根据谷歌学术中的热门出版物数据，2023年的视觉艺术学国际期刊排名正式发布。此次排名采用H5指数作为主要评定标准，为广大艺术学者和研究者提供了一个权威的参考。H5指数是指在过去整整5年中所发表文章的h指数。具体来说，这是一个衡量在2018-2022年间发表的h篇文章，每篇至少都被引用过h次的最大值。经过详细的数据统计和评估，以下为部分排名前列的期刊：

谷歌学术是世界上最大的学术期刊索引平台，该平台包含了绝大多数出版的学术期刊。2023年谷歌学术建筑学领域的期刊Top排名如下

本文对寒冷地区某高校一栋教学楼进行温湿度实测，并通过问卷对教室的热、声环境进行使用后评价，实测温度表明，多数教室基本达不到18℃，湿度也较低。主观问卷结果表明，与温度满意度相比，学生对噪声的满意度更低，消极影响更多。

Different from the existing empirical landscape measurement methods, this article explores an effective method to quantify the landscape through the use of computer vision perception. First, based on image semantic segmentation technology, a large number of excellent case images are put into image semantic segmentation and annotation tools for image segmentation. Secondly, the image semantic segmentation is used to quantitatively analyze the landscape space elements of the old urban site, and obtain the data value of each landscape element in the excellent case, which provides a reference for the design of the element ratio data basis. Finally, it is applied to the street space in the old city of Macau to perform a quantitative analysis of the computer vision perception of the streets in the old city. Get quantitative suggestions for landscape improvement, and provide new methods for quantitative research and transformation of old urban areas.