With the developments of data science and big data [
1], urban lifelines have paid more attention to addressing climate change [
2,
3]. As an essential component of urban lifelines, building comfort and structural safety monitoring plays an important role in analyzing the impact of climate change on urban environments and buildings [
4]. Building comfort can help to analyze the formation mechanism and evolution of the urban heat island effect. For example, most buildings in a city suffer from indoor overheating. In that case, these buildings are absorbing and storing a large amount of solar radiation, which increases the temperature around them and exacerbates the urban heat island effect. The monitoring of structural safety can be used to assess the relationship between urban building morphology and climate change; for example, strong winds may cause risks to buildings, and building collapse results in changes in building morphology, which affects changes in the wind field, which leads to changes in the climate [
5,
6,
7,
8]. However, long-term monitoring activities are bound to produce a complicated type of monitoring data [
9,
10,
11], such as point cloud data, BIM [
12], strain values, temperature and humidity values, and the tilt angle. How to effectively store and manage these monitoring data is a prerequisite for improving the efficiency of urban thermal environment management and maximizing its value for building monitoring [
13,
14]. Therefore, it is necessary to develop a spatial database system for monitoring building comfort and structural safety to improve the efficiency of monitoring data storage and management and to realize comprehensive analysis and management of building comfort and structural safety monitoring data. The system can help to analyze the formation mechanism and evolution of the urban heat island effect, assess the interrelationship between climate change and urban building form, promote the improvement of urban quality of life, mitigate the challenges posed by climate change, promote sustainable urban development, and build a more climate-resilient urban thermal environment.
1.1. Literature Review
In building monitoring, the methods of handling the large amount of monitoring data generated by long-term monitoring can be divided into the following main categories. Some researchers rely mainly on traditional data recording and analysis methods, which do not involve the study and use of databases; this type of research has limited storage and management of data, a large amount of data is stacked together, and lacks systematic data processing and querying capabilities, and is only applicable to the monitoring of a limited and more straightforward scale. Xue J et al. [
15] proposed a method to monitor the construction process of a high-rise building by identifying the unfinished building components from a top view through the target detection technique, comparing and registering them with the BIM components, and inferring the overall construction progress by counting the number of identified and registered components. Li X et al. [
16] designed a structural health monitoring system to monitor the overall condition of the South Building of East China Hospital and the condition of critical components to ensure structural safety during the overall lifting of the South Building of East China Hospital by 1.30 m to correct the settlement. Guo M et al. [
17] used UAV images and ground-based LiDAR point clouds for sweep detection of wooden towers and performed multi-period data comparisons to realize high-precision deformation monitoring of wooden towers. However, the paper did not deal with the data storage problem, and there was no database storage.
Some researchers have begun to utilize database technology to store data. However, these databases tend to be limited in size and have a single data storage content, which needs to be improved to respond to complex and diverse monitoring needs. Arslan M et al. [
18] developed a “real-time environmental monitoring, visualization, and notification system” using sensor technology integrated with BIM to monitor temperature and humidity sensor data in real-time to ensure worker health and safety in hot and humid environments. An SQL Server database was constructed, but there were only six data tables for building and room identification, sensor information, and threshold-triggered notification history. Valinejadshoubi M et al. [
19], developing an Internet of Things (IoT) and BIM-based automatic alarm system for thermal comfort monitoring of buildings, successfully developed a relational database system using MySQL for storing and updating the data collected by the sensors and associated sensor parameters. However, the database system contains only six data tables and is relatively small. Meng Q et al. [
20] developed an Internet of Things (IoT)-based sensing system for building vibration monitoring, in which a MySQL database was constructed for long-term data storage, mainly focusing on all the raw data from acceleration sensors, processed data, and analyzed results, with a relatively homogeneous storage content.
Many researchers have begun to build more complex monitoring databases for efficient storage and management of monitoring data to accomplish more complex monitoring needs. However, such databases do not have the function of spatial characterization or correlation, limiting their application’s scope in building monitoring. Liu T et al. [
21] designed a structural health monitoring (SHM) system using a variety of sensors to monitor different structural parameters that significantly affected the safety of the structure and established a database system to manage the monitoring data from nearly 600 sensors and developed structural monitoring, analysis, and evaluation software in conjunction with finite element software, which realizes the exchange of data and comparison of the results between on-site monitoring and numerical modeling, to evaluate the performance of the structure during construction. Liu G et al. [
22] proposed an information physical system (CPS)-based real-time monitoring and visualization method of greenhouse gas emissions from assembled buildings for real-time monitoring research of greenhouse gas emissions. They use MySQL to receive and store emissions data from remote servers and all kinds of related data. Yuan, S. et al. [
23] designed and implemented the mentioned remote wireless sensor network-based foundation settlement dynamic early warning system for high-rise buildings with lower computer hardware and software and an upper computer system to monitor the foundation settlement of high-rise buildings and constructed a SQL Server database to store all the monitoring data, which ensured the effective management of the foundation settlement detection data of high-rise buildings. Yang Q et al. [
24] designed and implemented a structural health management system for wood-framed ancient buildings, deploying 104 sensors of six different types to monitor environmental effects and structural responses and storing a large amount of data collected by the sensors and maintenance information in a SQL Server database to realize the structural health monitoring of the Feiyun Wooden Pavilion in China. Xu J et al. [
25] designed a conceptual model combining Building Information Modeling (BIM) and real-scene 3D models to effectively manage sensors and their corresponding monitoring data for structural health monitoring to ensure building safety. Moreover, the monitoring data of multiple sensors are stored in a database management system.
Regarding database technology, the demand for the corresponding databases, which support storing big data, is increasing with the continuous accumulation and rapid growth of data. Denton et al. [
26] discussed the advantages and disadvantages of commercial enterprise software and personal databases, such as Oracle, IBM’s DB2, Microsoft Access, PostgreSQL, and MySQL. Bravo et al. [
27] designed a document management system by applying MySQL and Apache2 Server to storage, control, management, and distribution. Peng et al. [
28] analyzed the functional requirements of college dormitories and designed a student dormitory management system using Java, SSM framework, and MySQL. Eyada M et al. [
29] evaluated the performance of two types of databases, MongoDB and MySQL, in cloud computing to handle different specifications of resources. MongoDB outperforms MySQL in terms of latency and database size and is more resource-efficient, and MySQL needs high-performance resources to work at a lower performance. Gyoroedi C et al. [
30] proposed methods to optimize database structure and queries and comparatively analyzed the impact of the optimization methods on each specific DBMS when performing CRUD (Create, Read, Update, Delete) requests. Zmaranda D et al. [
31] compared two popular open-source DBMS: relational MySQL and non-relational Elasticsearch. They proposed and implemented a data replication solution that imports data from the predominantly relational MySQL databases into Elasticsearch, which a MySQL document stores as a possible alternative for a more efficient data search. Yin P et al. [
32] developed a software system for an urban land planning database in Shanghai, China, based on MySQL; They established a platform with functions of data management, information sharing, and map assistance to realize efficient storage and management of land data.
1.2. Research Methodology
In China, most data generated from building monitoring has begun to be stored and managed using database technology. However, more research must be conducted on storing and managing multi-source data generated in building comfort and structural safety monitoring, especially on storing and managing spatial data such as 3D point clouds and BIM models. There is no spatial database specialized for building comfort and structural safety monitoring. At the same time, there are abundant database technologies to develop building comfort and structural safety monitoring databases. Moreover, a complete data management platform should contain primary data and a complete and practical database management system.
Therefore, in order to be able to efficiently store and manage the multi-source data generated in building comfort and structural safety monitoring, data support is provided for the study of the interrelationship between climate change and building comfort and structural safety. We propose an object-relational spatial database, design the conceptual and logical models of the spatial database for building comfort and structural safety monitoring, and discuss the entities, attributes, and connections in the model. Based on the conceptual and logical model, a spatial database management system with the functions of data storage, management, analysis, and visualization is established by adopting a mainstream backend framework and combining common database and programming language tools and BIM technology for development. The method for designing a spatial database is discussed in detail, and the main functional modules are described. In addition, integrating BIM technology into the spatial database management system enhances the monitoring data’s application value and the real-time nature of the condition information. It helps the in-depth analysis and efficient management of building comfort and structural safety monitoring data and further promotes the development and application of building monitoring technology.
The contributions of this study are the following: (1) The conceptual model and logical model of the spatial database for building comfort and structural safety monitoring are proposed, and the object-relational spatial database for building comfort and structural safety monitoring is designed, which is targeted to solve the problem of storing and managing the massive monitoring data generated in the process of building comfort and structural safety monitoring. (2) BIM technology is integrated into the spatial database management system, and the management and visualization functions of BIM-based building monitoring data are designed and implemented to achieve a high degree of visualization and interaction of monitoring data on the website, which promotes the application and development of building monitoring technology.