Research Article Testing the Effects of the Digital Linguistic Landscape on Engineering Education for Smart Construction Lin Xu, 1 Jingxiao Zhang , 2 Yin Yuan, 1 Junwei Zheng, 3 Simon P. Philbin, 4 Brian H. W. Guo, 5 and Ruoyu Jin 6 1 School of Foreign Languages, Northwest University, Xi’an 710127, China 2 School of Economics and Management, Chang’an University, Xi’an 710064, China 3 Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, China 4 School of Engineering, London South Bank University, London SE10AA, UK 5 Department of Civil & Natural Resources Engineering, University of Canterbury, Christchurch, New Zealand 6 School of Built Environment and Architecture, South Bank University, London, UK Correspondence should be addressed to Jingxiao Zhang; zhangjingxiao964@126.com Received 9 February 2022; Revised 17 March 2022; Accepted 8 April 2022; Published 28 May 2022 Academic Editor: Huihua Chen Copyright © 2022 Lin Xu et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. is study investigates the mechanism of digital linguistic landscapes in enabling engineering education for smart construction according to the educational dimensions of A (ability), S (skill), and K (knowledge). A questionnaire survey was conducted based on the core concepts of the informative dimension and symbolic dimension in digital language landscape as well as the ability dimension, knowledge dimension, and skill dimension in engineering education. Structural equation modeling (SEM) was used as the test method. e results of the research demonstrate that the informative dimension and symbolic dimension are two main aspects of DLL in education of engineering students for smart construction. Additionally, DLL has a significant positive impact on the ability, knowledge, and skill education of engineering students for smart construction. e research has theoretical and practical significance, as it not only enriches research on the relationship between DLL and engineering education for smart construction but also expands the theoretical understanding of engineering education from the perspective of linguistics. Furthermore, the study explores the path of the practical application of digital language landscape to engineering education for smart construction. 1. Introduction e construction industry faces a number of pressing challenges, including improving the level of productivity and responding to the high level of fragmentation and com- plexity as well as leveraging the emerging opportunities by digital transformation [1, 2]. Indeed, the construction in- dustry has already started to adopt digital technologies to improve the operational performance of industrial activities, including virtual reality, Internet of ings, and machine learning [3]. Moreover, the requirement for smart buildings is rapidly becoming an inherent constituent of policies as- sociated with the design and development of buildings for the future [4]. erefore, smart construction has been proposed as a new concept for the construction industry to adopt in order that the sector can fully capitalize on the opportunities afforded by digital transformation. Smart construction systems have huge potential to improve the efficiency of construction industry [5]. Specifically, they empower the engineering production system and promote the interconnection of engineering construction processes, including online and offline integration, resource, and ele- ment collaboration [6, 7]. In this context, the 21st century engineers and architects must be able to deal with the rapid pace of technological change and successfully navigate the highly interconnected world of industry [8]. Facing such profound changes in the construction industry triggered by digital technologies in the new era, there is an increasing Hindawi Computational Intelligence and Neuroscience Volume 2022, Article ID 4077516, 18 pages https://doi.org/10.1155/2022/4077516