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This paper aims to design a novel feature descriptor to improve the performance of feature matching in challenge scenes, such as low texture and wide-baseline scenes. Common descriptors are not suitable for low texture scenes and other challenging scenes mainly owing to encoding only one kind of features. The proposed feature descriptor considers multiple features and their locations, which is more expressive.

A graph neural network–based descriptors enhancement algorithm for feature matching is proposed. In this paper, point and line features are the primary concerns. In the graph, commonly used descriptors for points and lines constitute the nodes and the edges are determined by the geometric relationship between points and lines. After the graph convolution designed for incomplete join graph, enhanced descriptors are obtained.

Experiments are carried out in indoor, outdoor and low texture scenes. The experiments investigate the real-time performance, rotation invariance, scale invariance, viewpoint invariance and noise sensitivity of the descriptors in three types of scenes. The results show that the enhanced descriptors are robust to scene changes and can be used in wide-baseline matching.

A graph structure is designed to represent multiple features in an image. In the process of building graph structure, the geometric relation between multiple features is used to establish the edges. Furthermore, a novel hybrid descriptor for points and lines is obtained using graph convolutional neural network. This enhanced descriptor has the advantages of both point features and line features in feature matching.

Green construction is increasingly vital in promoting sustainability within the construction industry. The development and promotion of green construction technologies are central to this endeavor. However, existing evaluations mainly target building components, construction projects or certain construction processes. There is a notable absence of research into the greenness of construction technologies. Assessing the greenness of construction technologies is crucial for streamlining resource utilization and reducing waste. To address this gap, this study aims to establish a Greenness of Construction Technologies (GCT) evaluation model using the method of analytic network process (ANP).

Green construction is increasingly vital in promoting sustainability within the construction industry. The development and promotion of green construction technologies are central to this endeavor. However, existing evaluations mainly target building components, construction projects or certain construction processes. There is a notable absence of research into the greenness of construction technologies. Assessing the greenness of construction technologies is crucial for streamlining resource utilization and reducing waste. To address this gap, this study establishes a GCT evaluation model using the method of ANP.

Among the four formwork technologies of plastic, steel, plywood and wooden formwork, the plastic formwork exhibits the best performance in terms of environmental friendliness, economic and social effects, while facing issues like material wastage and low static strength. The results align with practical observations which validates the model’s applicability.

This research contributes to the field by introducing the concept of greenness into construction technology evaluation for the first time. The establishment of the GCT evaluation model promotes the adoption of green construction technologies and advances sustainable practices in the construction industry.

The purpose of this paper is to establish a maturity evaluation model for the application of construction steel structure welding robotics suitable for the actual situation and specific characteristics of engineering projects in China and then to assess the maturity level of the technology in the application of domestic engineering projects more scientifically.

The research follows a qualitative and quantitative analysis method. In the first stage, the structure of the maturity model is constructed and the evaluation index system is designed by using the ideas of the capability maturity model and WSR methodology for reference. In the second stage, the design of the evaluation process and the selection of evaluation methods (analytic hierarchy process method, multi-level gray comprehensive evaluation method). In the third stage, the data are collected and organized (preparation of questionnaires, distribution of questionnaires, questionnaire collection). In the fourth stage, the established maturity evaluation model is used to analyze the data.

The evaluation model established by using multi-level gray theory can effectively transform various complex indicators into an intuitive maturity level or score status. The conclusion shows that the application maturity of building steel structure welding robot technology in this project is at the development level as a whole. The maturity levels of “WuLi – ShiLi – RenLi” are respectively: development level, development level, between starting level and development level. Comparison of maturity evaluation values of five important factors (from high to low): environmental factors, technical factors, management factors, benefit factors, personnel and group factors.

In this paper, based on the existing research related to construction steel structure welding robot technology, a quantitative and holistic evaluation of the application of construction steel structure welding robot technology in domestic engineering projects is conducted for the first time from a project perspective by designing a maturity evaluation index system and establishing a maturity evaluation model. This research will help the project team to evaluate the application level (maturity) of the welding robot in the actual project, identify the shortcomings and defects of the application of this technology, then improve the weak links pertinently, and finally realize the gradual improvement of the overall application level of welding robot technology for building steel structure.