Food storage helps to ensure the food consumption needs of non-agricultural populations and to respond to major natural disasters or other emergencies, and the application of food storage technology can reduce post-harvest food losses. However, there are still obvious shortcomings in coping with large grain losses. Therefore, quantitative analysis of the research hotspots and evolutionary trends of grain storage technology is important to help the development of grain storage technology. This article uses the Web of Science database from 2007 to 2022 as a data sample with the help of CiteSpace software to analyze the basic situation, research hotspots, and evolutionary trends to draw a series of relevant knowledge maps. Visual analysis revealed that the number of publications had grown rapidly since 2015. First, the Journal of Stored Products Research, Journal of Economic Entomology, and Journal of Agricultural and Food Chemistry, with citation frequencies of 929, 536, and 453, should be focused on in order to keep up with the latest research developments in this field. The United States, China, and Brazil occupy dominant positions in relation to grain storage technology studies in general. Purdue University, Kansas State University, and Agricultural Research Institute ranked the top three in terms of the number and centrality of publications. In terms of research hotspots, the centrality of temperature, insects, carbon dioxide, and quality were 0.16, 0.09, 0.08, and 0.08. It shows that the field of grain storage technology in recent years has focused on grain storage temperature, pest control, and grain storage quality research. From the perspective of the evolution trend, the life cycle of emergent words lasts for several years, after which the strength of emergent words slowly decreases and is replaced by new emergent words. Mortality was the first keyword to appear and remained from 2007 to 2011, indicating that research on fumigants and their toxicity, as well as pest mortality under air fumigation and chemical fumigation conditions, became more popular during this period. In recent years, new terms have emerged that had never been used before, such as “grain quality” (2019–2022) and “stability” (2020–2022). We can find that people pursue food quality more with the improvement of people’s living standards. In this context, future research should seek more efficient, safe, economical, and environmentally friendly methods of grain storage and continuously improve the level of scientific grain storage.

As an emerging method in engineering construction, concrete 3D printing has experienced rapid development in recent years due to its advantages in terms of automation, digitization, and intelligence. In order to comprehensively understand the research progress of concrete 3D printing technology, the literature on concrete 3D printing technology published in the Web of Science core database from 2014 to 2023 is used as a data source, and the visualization tool CiteSpace is utilized to research and analyze these results from the following aspects: number of publications, collaboration networks, research hotspots, and evolutionary trends. The results show that the number of publications in the field of concrete 3D printing technology has been increasing from year to year, with a gradually accelerating growth rate. The top three journals in terms of publication volume are Construction and Building Materials, Cement and Concrete Composites, and Materials. Research in the field of concrete 3D printing technology is mainly conducted through collaborations among research groups or institutions, while there are relatively fewer interactions among researchers from different institutions, regions, and countries. The current research direction mainly focuses on 3D printing materials, process parameters, and technology. The research content has progressed from initial explorations of concrete 3D printing technology to detailed studies of 3D printing materials and their performance. Overall, the research in this field continues to add further details to our knowledge of concrete 3D printing technology, and its application in engineering is gradually increasing. The automated, digitized, and intelligent construction methods of concrete 3D printing technology serve as a powerful driving force for the transformation and upgrading of the field of architecture.

Big data technology has triggered a boom in research and applications around the world. The construction industry has ushered in a new technological change in this context. Researchers have conducted in-depth research on the intersection of big data and architecture, but lack quantitative analysis and comprehensive evaluation of the research results. This article draws a series of knowledge maps with the help of the CiteSpace software using the relevant literature in the Web of Science database between 2007 and 2022 as data samples to comprehensively grasp the research development at the intersection of big data and the construction industry. The knowledge base, research hotspots, and domain evolution trends in the intersection of big data and the construction industry are analyzed quantitatively and aided by qualitative analysis through visualization, respectively. The results show that Chinese and American scholars have published more relevant papers in international journals, and some well-known universities in both countries constitute the main group of research institutions. The research hotspots are BIM, data mining, building energy saving, smart cities, and disaster prevention and damage prevention. In the future, the research on the integration and application of the construction industry with emerging technologies, such as big data, BIM, and cloud computing will be connected more closely. This study provides a preliminary overall picture of the research of big data in the field of construction by sorting out and analyzing the existing results.

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.