The core driving force behind innovation in intelligent construction technology is synergistic relationships. It has become common practice to promote synergistic innovation through agent interaction and knowledge coupling in the development of intelligent construction technology. Drawing upon synergetics, social network theory, and the knowledge base view as theoretical frameworks, this research examines the impact of synergistic relationship, agent interaction, and knowledge coupling on innovation in intelligent construction technology. An empirical analysis of 186 questionnaires revealed the following: (1) regarding synergistic relationships, both horizontal synergy and vertical synergy significantly positively impact innovation in intelligent construction technology. (2) Concerning agent interaction, strong interaction serves as a mediator between horizontal synergy and innovation in intelligent construction technology, while weak interaction serves as a mediator between vertical synergy and innovation in intelligent construction technology. (3) Knowledge coupling has a positive moderating effect on innovation in intelligent construction technology under a strong interaction and a negative moderating effect on innovation in intelligent construction technology under a weak interaction. This study contributes to expanding the theory of synergistic relationships and its application in the context of intelligent construction technology. Furthermore, it provides practical insights and guidance for construction companies seeking to enhance innovation in intelligent construction technology through the utilization of agent interaction and knowledge coupling.

Corrosion detection plays a crucial role in the effective lifecycle management of steel structures, significantly impacting maintenance strategies and operational performance. This study presents a machine vision-based approach for classifying corrosion levels in Q235 steel, providing valuable insights for lifecycle assessment and decision-making. Accelerated salt spray tests were performed to simulate corrosion progression over multiple cycles, resulting in a comprehensive dataset comprising surface images and corresponding eight loss measurements. A comparative evaluation with other architectures, namely, AlexNet, ResNet, and VggNet, demonstrated that the Inception v3 model achieved superior classification accuracy, exceeding 95%. This method offers an effective and precise solution for corrosion evaluation, supporting proactive maintenance planning and optimal resource allocation throughout the lifecycle of steel structures. By leveraging advanced deep learning techniques, the approach provides a scalable and efficient framework for enhancing the sustainability and safety of steel infrastructure.

A shortage of skilled laborers has constrained the new development path called Construction 4.0, which is proposed to improve the construction industry with advanced technologies. It should be noted that the mismatch between labor skills and technological progress will harm the sustainable development of the construction sector. In China, a three-tier management structure consisting of contractors, labor subcontractors, and laborers is widespread in the labor market. Considering the relationship between laborers and construction firms, promoting construction labor professionalization depends on the cooperation of general contractors and labor subcontractors. Previous studies have focused on training techniques and methods, but have neglected to investigate whether stakeholders are willing to cooperate in training labor. Considering that the standard workforce is more likely to receive human resource investment, this paper aims to analyze the strategic choice of related stakeholders in cultivating the standard workforce. Since evolutionary game theory has proven to be an effective method to study the symbiotic evolution between stakeholders, this paper will apply it to develop an evolutionary game model and conduct analysis. Moreover, this research examines how government incentive policy affects the dynamic evolution process of cultivating a standard workforce. The results suggest that without government incentives, general contractors and labor subcontractors tend to choose a contingent workforce. When the government subsidy reaches a certain level, general contractors and labor subcontractors are willing to cooperate in cultivating a standard workforce. Additionally, it is worth noting that compensation for labor subcontractors is relatively more effective than that for general contractors in promoting construction labor professionalization. Furthermore, some suggestions and countermeasures are proposed to help to cultivate a standard workforce in China.

Intelligent corrosion diagnosis plays a crucial role in enhancing the efficiency of operation and maintenance for steel structures. Presently, corrosion detection primarily depends on manual visual inspections and non-destructive testing methods, which are inefficient, costly, and subject to human bias. While machine vision has demonstrated significant potential in controlled laboratory settings, most studies have focused on environments with limited background interference, restricting their practical applicability. To tackle the challenges posed by complex backgrounds and multiple interference factors in field-collected images of steel components, this study introduces an intelligent corrosion grading method designed specifically for images containing background elements. By integrating an attention mechanism into the traditional U-Net network, we achieve precise segmentation of component pixels from background pixels in engineering images, attaining an accuracy of up to 94.1%. The proposed framework is validated using images collected from actual engineering sites. A sliding window sampling technique divides on-site images into several rectangular windows, which are filtered based on U-Net Attention segmentation results. Leveraging a dataset of steel plate corrosion images with known grades, we train an Inception v3 corrosion classification model. Transfer learning techniques are then applied to determine the corrosion grade of each filtered window, culminating in a weighted average to estimate the overall corrosion grade of the target component. This study provides a quantitative index for assessing large-scale steel structure corrosion, significantly impacting the improvement of construction and maintenance quality while laying a solid foundation for further research and development in related fields.

With the development of drone and image recognition technologies, using drones to capture images for engineering structural damage detection can replace inefficient and unsafe manual maintenance inspections. This paper focuses on the pairing method between drone devices and the BIM components of large buildings, with Revit’s secondary development serving as the technical approach. A plugin for pairing drone images with BIM components is developed. The research first establishes the technical scheme for pairing drone images with BIM models. Then, the positional and directional information of the drone images are extracted, and a reference coordinate system for the drone’s position and image capture orientation is introduced. The transformation method and path from the real-world coordinate system to the Revit 2023 software coordinate system are explored. To validate the interactive logic of the transformation path, a pairing plugin is developed in Revit. By employing coordinate conversion and Revit family loading procedures, the relative position and capture orientation of the drone are visualized in the 3D BIM model. The plugin uses techniques such as family element filtering and ray tracing to automatically identify and verify BIM components, ensuring the precise matching of drone images and BIM components. Finally, the plugin’s functionality is verified using a high-rise building in Wuhan as a case study. The results demonstrate that this technological approach not only improves the efficiency of pairing drone images with models in building smart maintenance but also provides a fast and reliable method for pairing drones with BIM systems in building management and operations. This contributes to the intelligent and automated development of building maintenance.

Recently, rapid urbanization around the world has spawned several urban problems. Although a large amount of experience has been accumulated throughout the process of global urban problem governance, the knowledge has not been optimally utilized. Furthermore, there is a dearth of mechanisms with which to distill and employ past experiences in addressing emerging urban problems. Consequently, in this study, based on the CBR method, we establish a mechanism called the Solution-Extracted System of Urban Problem Governance (SESUPG), aiming to find solutions to the diverse array of existing urban problems from previous experience. The main steps for obtaining a suitable solution for a specific urban problem in a target city through the SESUPG are as follows: (1) Calculate the similarity to retrieve the most similar cities. (2) Extract the possible solution through similar cities. (3) Case–solution modification before solution adoption. To verify the effectiveness of the proposed mechanism, the air pollution problem in Wuhan, China, was tested to verify the effectiveness of the SESUPG as a case study. As a result, four policy recommendations were extracted by the SESUPG, and all of them proved to be effective in mitigating air pollution problems in Wuhan. The system proposed in this study can aid decision makers in the selection of strategies and solutions when addressing urbanization issues and guiding the process of mining effective experience for the promotion of urban governance levels.