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The civil engineering industry faces with a vast array of unstructured textual information during its digital transformation. Large language models (LLMs) provide a new opportunity for the intelligent transformation of the industry because of its powerful natural language processing capability. A systematic literature review approach was employed, and based on the technical framework of LLMs and the current state of research in vertical domains, four major application scenarios for LLMs in civil engineering were suggested, along with corresponding technological approaches, challenges faced, and research trends. It is found that exploratory research and application of LLMs in civil engineering have been conducted, primarily focusing on content creation, intelligent Q & A, text summarization, and analytical reasoning, covering the entire lifecycle of civil engineering projects and featuring interdisciplinary and multimodal integration. However, the utilization of LLMs struggles with low specificity of knowledge, poor timeliness of information, and inferior data quality and interactivity. Based on this, a series of future research opportunities were proposed to enhance the breadth and depth of LLMs application in the field of civil engineering by using parametric efficient fine-tuning technology to inject expertise in model optimization. Combined with knowledge graph, LLMs can improve the accuracy, interpretability and timeliness of answers. Multi-modal data types were integrated to expand the application scenarios of LLMs in civil engineering. Appropriate model evaluation methods were developed to quantify the value and performance of LLMs applications in civil engineering. In terms of application scenarios, combined with the characteristics of LLMs and civil engineering fields, the application of LLMs in complex tasks such as document generation, question and answer system, information extraction and compliance review can be expanded, and the interaction efficiency between practitioners and data can be improved. The purpose of the study is to provide reference for the academic and business circles to further apply LLMs in the field of civil engineering.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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