期刊：CivilEng

主页：https://www.mdpi.com/journal/civileng

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1.Optimizing the Utilization of Generative Artificial Intelligence (AI) in the AEC Industry: ChatGPT Prompt Engineering and Design

优化生成式人工智能 (AI) 在建筑、工程和施工 (AEC) 行业的应用：ChatGPT 提示工程和设计

https://www.mdpi.com/2673-4109/5/4/49

Samsami, R. Optimizing the Utilization of Generative Artificial Intelligence (AI) in the AEC Industry: ChatGPT Prompt Engineering and Design. CivilEng 2024, 5, 971-1010. https://doi.org/10.3390/civileng5040049

2.Hybrid Topology Optimization of a Concrete Structure via Finite Element Analysis and Deep Learning Surrogates

基于有限元分析和深度学习代理的混凝土结构混合拓扑优化

https://www.mdpi.com/2673-4109/6/4/68

Gindy, M.; Abbas, M.M.; Muntean, R.; Butnariu, S. Hybrid Topology Optimization of a Concrete Structure via Finite Element Analysis and Deep Learning Surrogates. CivilEng 2025, 6, 68. https://doi.org/10.3390/civileng6040068

3.Promoting the Application of Big Data in Construction through Stakeholder Collaboration Based on a Two-Mode Network

基于双模网络，通过利益相关者协作促进大数据在建筑领域的应用

https://www.mdpi.com/2673-4109/5/3/34

Wang, Y.; Zhang, Y.; Wang, H.; Meng, Q.; Zhai, Y.; Dong, N. Promoting the Application of Big Data in Construction through Stakeholder Collaboration Based on a Two-Mode Network. CivilEng 2024, 5, 629-645. https://doi.org/10.3390/civileng5030034

4.A Review of the Application of Artificial Intelligence in Climate Change-Induced Flooding—Susceptibility and Management Techniques

人工智能在气候变化引发的洪灾——易发性和管理技术中的应用综述

https://www.mdpi.com/2673-4109/5/4/58

David, A.O.; Ndambuki, J.M.; Muloiwa, M.; Kupolati, W.K.; Snyman, J. A Review of the Application of Artificial Intelligence in Climate Change-Induced Flooding—Susceptibility and Management Techniques. CivilEng 2024, 5, 1185-1198. https://doi.org/10.3390/civileng5040058

5.Weighting Variables for Transportation Assets Condition Indices Using Subjective Data Framework

利用主观数据框架对交通运输资产状况指数的变量进行加权

https://www.mdpi.com/2673-4109/5/4/48

Al-Hamdan, A.B.; Alatoom, Y.I.; Nlenanya, I.; Smadi, O. Weighting Variables for Transportation Assets Condition Indices Using Subjective Data Framework. CivilEng 2024, 5, 949-970. https://doi.org/10.3390/civileng5040048

6.Neural Network Prediction and Enhanced Strength Properties of Natural Fibre-Reinforced Quaternary-Blended Composites

神经网络预测及天然纤维增强四元共混复合材料强度性能提升

https://www.mdpi.com/2673-4109/5/4/43

Chandramouli, P.; Akthar, M.R.N.; Kumar, V.S.; Jayaseelan, R.; Pandulu, G. Neural Network Prediction and Enhanced Strength Properties of Natural Fibre-Reinforced Quaternary-Blended Composites. CivilEng 2024, 5, 827-851. https://doi.org/10.3390/civileng5040043

7.Application of Machine Learning for Real-Time Structural Integrity Assessment of Bridges

机器学习在桥梁实时结构完整性评估中的应用

https://www.mdpi.com/2673-4109/6/1/2

Jayasinghe, S.; Mahmoodian, M.; Alavi, A.; Sidiq, A.; Sun, Z.; Shahrivar, F.; Setunge, S.; Thangarajah, J. Application of Machine Learning for Real-Time Structural Integrity Assessment of Bridges. CivilEng 2025, 6, 2. https://doi.org/10.3390/civileng6010002

8.Explainable Machine Learning to Predict the Construction Cost of Power Plant Based on Random Forest and Shapley Method

基于随机森林和Shapley方法的可解释机器学习在预测电厂建设成本中的应用

https://www.mdpi.com/2673-4109/6/2/21

Alazawy, S.F.M.; Ahmed, M.A.; Raheem, S.H.; Imran, H.; Bernardo, L.F.A.; Pinto, H.A.S. Explainable Machine Learning to Predict the Construction Cost of Power Plant Based on Random Forest and Shapley Method. CivilEng 2025, 6, 21. https://doi.org/10.3390/civileng6020021

9.Recursive Time Series Prediction Modeling of Long-Term Trends in Surface Settlement During Railway Tunnel Construction

铁路隧道施工期间地表沉降长期趋势的递归时间序列预测模型

https://www.mdpi.com/2673-4109/6/2/19

Zhang, F.; Wei, Q.; Wu, Z.; Cao, J.; Jian, D.; Xiang, L. Recursive Time Series Prediction Modeling of Long-Term Trends in Surface Settlement During Railway Tunnel Construction. CivilEng 2025, 6, 19. https://doi.org/10.3390/civileng6020019

10.Digital-Twin-Based Structural Health Monitoring of Dikes

基于数字孪生的堤坝结构健康监测

https://www.mdpi.com/2673-4109/6/3/39

Bornholdt, M.; Herbrand, M.; Smarsly, K.; Zehetmaier, G. Digital-Twin-Based Structural Health Monitoring of Dikes. CivilEng 2025, 6, 39. https://doi.org/10.3390/civileng6030039

11.Machine Learning-Based Compressive Strength Prediction in Pervious Concrete

基于机器学习的透水混凝土抗压强度预测

https://www.mdpi.com/2673-4109/7/1/3

Baseer, H.A.; Ali, G.G.M.N. Machine Learning-Based Compressive Strength Prediction in Pervious Concrete. CivilEng 2026, 7, 3. https://doi.org/10.3390/civileng7010003

12.Synergic Co-Benefits and Value of Digital Technology Enablers for Circular Management Models Across Value Chain Stakeholders in the Built Environment

数字技术赋能者在建筑环境价值链各利益相关者中实现循环管理模式的协同效益和价值

https://www.mdpi.com/2673-4109/6/4/62

Kaewunruen, S.; Baniotopoulos, C.; Teuffel, P.; Driou, H.; Valta, O.; Pešta, J.; Bajare, D. Synergic Co-Benefits and Value of Digital Technology Enablers for Circular Management Models Across Value Chain Stakeholders in the Built Environment. CivilEng 2025, 6, 62. https://doi.org/10.3390/civileng6040062

13.A Modular, Logistics-Centric Digital Twin Framework for Construction: From Concept to Prototype

面向建筑行业的模块化、以物流为中心的数字孪生框架：从概念到原型

https://www.mdpi.com/2673-4109/6/4/59

Gehring, M.; Brötzmann, J.; Rüppel, U. A Modular, Logistics-Centric Digital Twin Framework for Construction: From Concept to Prototype. CivilEng 2025, 6, 59. https://doi.org/10.3390/civileng6040059

14.Data-Driven Optimization of Sustainable Asphalt Overlays Using Machine Learning and Life-Cycle Cost Evaluation

基于机器学习和生命周期成本评估的可持续沥青罩面数据驱动优化

https://www.mdpi.com/2673-4109/7/1/1

Kashesh, G.J.; Joni, H.H.; Dulaimi, A.; Kaishesh, A.J.; Al-Saeedi, A.A.K.; Ribeiro, T.P.; Bernardo, L.F.A. Data-Driven Optimization of Sustainable Asphalt Overlays Using Machine Learning and Life-Cycle Cost Evaluation. CivilEng 2026, 7, 1. https://doi.org/10.3390/civileng7010001

15.Application of Machine Learning for Predicting Seismic Damage in Base-Isolated Reinforced Concrete Buildings

机器学习在预测隔震钢筋混凝土建筑地震损伤中的应用

https://www.mdpi.com/2673-4109/7/1/4

Algamati, M.; Al-Sakkaf, A.; Bagchi, A. Application of Machine Learning for Predicting Seismic Damage in Base-Isolated Reinforced Concrete Buildings. CivilEng 2026, 7, 4. https://doi.org/10.3390/civileng7010004

CivilEng 期刊介绍

主编：Angelo Luongo, University of L’Aquila, Italy

期刊专注于土木工程领域的最新研究进展，研究主题包括但不限于：结构工程、地震工程、建筑材料、建筑施工管理、建筑信息化、风险管理、交通工程、水资源与海岸工程等。期刊目前已被Scopus、ESCI (Web of Science)、Ei Compendex等数据库收录。

2024 Impact Factor: 2.0

2024 CiteScore: 4.0

Time to First Decision: 21.7 Days

Acceptance to Publication: 5.6 Days