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Toward Sustainable Development: The Application of Artificial Intelligence in Civil Engineering

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 9378

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Special Issue Editors

Dr. Minh-Tu Cao

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Guest Editor

Department of Civil Engineering, National Yang Ming Chiao Tung University, Hsinchu City 300, Taiwan
Interests: civil engineering; construction management; artificial intelligence; machine/deep learning; metaheuristic optimization

Dr. Ngoc-Mai Nguyen

E-Mail Website
Guest Editor

Institute and Department of Civil Engineering and Environmental Informatics, Minghsin University of Science and Technology, Hsinchu County 30401, Taiwan
Interests: civil engineering; construction management; artificial intelligence; data mining; computational intelligence

Special Issue Information

Dear Colleagues,

Prompted by the availability of data in civil engineering and construction management (CECM), researchers have conducted studies to apply artificial intelligence (AI) techniques to practical problems, paving the way for AI to be used in CECM, playing a pivotal role in the process of making construction “smart”. Importantly, AI techniques can be used to extract insightful information from the large volume of heterogeneous data that is burdened with high-dimensional non-linearity. Additionally, the wide availability of high-level, open-source frameworks has allowed researchers to design and train extremely powerful models, enabling AI to become integral to CECM procedures. Research into the application of AI in construction has the potential to open an array of opportunities, such as site supervision, automatic detection, structural design, and intelligent maintenance.

We welcome artificial intelligence studies in the field of civil engineering, including but not limited to structural engineering, construction management, hydrology, hydraulic engineering, geotechnical engineering, environmental engineering, transportation engineering, coastal and ocean engineering, and materials of construction. Additionally, we look forward to comprehensive review articles on using AI in addressing practical problems in civil engineering (machine learning, deep learning, fuzzy systems, expert systems, metaheuristic optimization, etc.).

Potential topics include but are not limited to the following:

Machine learning for mechanical problems in civil engineering;
Optimization models for single- and multi-objective solutions for construction management;
Metaheuristic optimization in civil engineering and construction management;
AI for smart cities;
Application of advanced artificial intelligence approaches for hydraulic problems;
Deep-learning- and machine-learning-based methods for structural defect detection;

We look forward to receiving your contributions.

Dr. Minh-Tu Cao
Dr. Ngoc-Mai Nguyen
Guest Editors

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Keywords

civil engineering
construction management
hydraulic engineering
reinforced concrete
structural defect detection
steel structure
structural engineering
artificial intelligence
machine learning
deep learning
metaheuristic optimization

Published Papers (5 papers)

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Research

16 pages, 7650 KiB

Open AccessArticle

Using Ground-Penetrating Radar and Deep Learning to Rapidly Detect Voids and Rebar Defects in Linings

by Peng Liu, Zude Ding, Wanping Zhang, Zhihua Ren and Xuxiang Yang

Sustainability 2023, 15(15), 11855; https://doi.org/10.3390/su151511855 - 01 Aug 2023

Cited by 2 | Viewed by 918

Abstract

The geological radar method has found widespread use in evaluating the quality of tunnel lining. However, relying on manual experience to interpret geological radar data may cause identification errors and reduce efficiency when dealing with large numbers of data. This paper proposes a method for identifying internal quality defects in tunnel lining using deep learning and transfer learning techniques. An experimental physical model for detecting the quality of tunnel lining radars was developed to identify the typical radar image features of internal quality defects. Using the geological radar method, a large volume of lining quality detection radar image data was collected, in conjunction with several examples of tunnel engineering. The preprocessing of geological radar data was performed, including gain and normalization, and a set of data samples exhibiting typical lining quality defects was prepared with 6236 detection targets in 4246 images. The intelligent recognition models for tunnel lining quality defects were established using a combination of geological radar image datasets and transfer learning concepts, based on the SSD and YOLOv4 models. The accuracy of the SSD algorithm for cavity defect recognition is 86.58%, with the YOLOv4 algorithm achieving slightly lower accuracy at 86.05%. For steel bar missing recognition, the SSD algorithm has an accuracy of 97.7%, compared to 98.18% accuracy for the YOLOv4 algorithm. This indicates that deep learning-based models are practical for tunnel quality defect detection. Full article

(This article belongs to the Special Issue Toward Sustainable Development: The Application of Artificial Intelligence in Civil Engineering)

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42 pages, 3655 KiB

Open AccessArticle

Investigating the Role of Artificial Intelligence Technologies in the Construction Industry Using a Delphi-ANP-TOPSIS Hybrid MCDM Concept under a Fuzzy Environment

by Ke Wang, Ziyi Ying, Shankha Shubhra Goswami, Yongsheng Yin and Yafei Zhao

Sustainability 2023, 15(15), 11848; https://doi.org/10.3390/su151511848 - 01 Aug 2023

Cited by 7 | Viewed by 3035

Abstract

The construction business is always changing, and with the introduction of artificial intelligence (AI) technology it is undergoing substantial modifications in a variety of areas. The purpose of this research paper is to investigate the function of AI tools in the construction industry using a hybrid multi-criteria decision-making (MCDM) framework based on the Delphi method, analytic network process (ANP), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) under a fuzzy scenario. The ANP framework offers a systematic approach to quantifying the relative importance of AI technologies based on expert opinions gathered during the Delphi process, whereas the fuzzy TOPSIS methodology is used to rank and select the most appropriate AI technologies for the construction industry. The final results from the ANP revealed that the technological factors are the most crucial, followed by the environmental factors, which highly influence the AI environment. In addition, TOPSIS identified robotics and automation as the best AI alternative among the three options, followed by building information modeling (BIM), whereas computer vision was the least preferred among the list. The proposed hybrid MCDM framework enables a comprehensive evaluation and selection process that takes into account the interdependencies between AI technologies and uncertainties in decision-making. Full article

(This article belongs to the Special Issue Toward Sustainable Development: The Application of Artificial Intelligence in Civil Engineering)

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14 pages, 3953 KiB

Open AccessArticle

Deep Learning-Based Automatic Defect Detection Method for Sewer Pipelines

by Dongming Shen, Xiang Liu, Yanfeng Shang and Xian Tang

Sustainability 2023, 15(12), 9164; https://doi.org/10.3390/su15129164 - 06 Jun 2023

Cited by 2 | Viewed by 1211

Abstract

To address the issues of low automation, reliance on manual screening by professionals, and long detection cycles in current urban drainage pipeline defect detection, this study proposes an improved object detection algorithm called EFE-SSD (enhanced feature extraction SSD), based on the SSD (Single Shot MultiBox Detector) network. Firstly, the RFB_s module is added to the SSD backbone network to enhance its feature extraction capabilities. Additionally, multiple scale features are fused to improve the detection performance of small target defects to some extent. Then, the improved ECA attention mechanism is used to adjust the channel weights of the output layer, suppressing irrelevant features. Finally, the Focal Loss is employed to replace the cross-entropy loss in the SSD network, effectively addressing the issue of imbalanced positive and negative samples during training. This increases the weight of difficult-to-classify samples during network training, further improving the detection accuracy of the network. Experimental results show that EFE-SSD achieves a detection mAP of 92.2% for four types of pipeline defects: Settled deposits, Displaced joints, Deformations, and Roots. Compared to the SSD algorithm, the model’s mAP was increased by 2.26 percentage points—ensuring the accuracy of pipeline defect detection. Full article

(This article belongs to the Special Issue Toward Sustainable Development: The Application of Artificial Intelligence in Civil Engineering)

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15 pages, 3892 KiB

Open AccessArticle

Potential of Repurposing Recycled Concrete for Road Paving: Flexural Strength (FS) Modeling by a Novel Systematic and Evolved RF-FA Model

by Shuwei Gu, Hao Shen, Chuming Pang, Zhiping Li, Long Liu, Huan Liu, Shuai Wang, Yaxin Song and Jiandong Huang

Sustainability 2023, 15(4), 3749; https://doi.org/10.3390/su15043749 - 17 Feb 2023

Viewed by 1090

Abstract

Concrete can be recycled after certain processing technologies for use in pavement engineering but the flexural strength (FS) is difficult to predict accurately in the design process. This study proposes a novel systematic and evolved approach to estimate the FS of recycled concrete. The proposed methods are conducted based on the random forest (RF) model as well as the firefly algorithm (FA), where the latter is employed to tune the hyperparameters of the RF model. For this purpose, data sets were collected from previously published literature for the training and verification of the model, and the accuracy of the model was verified by the fitting effect of the predicted and actual values. The results showed that the proposed hybrid machine learning model has a good fitting effect on the predicted and actual values; the calculation and evaluation process demonstrated fast convergence and significantly lower values of RMSE for the proposed model to determine the FS of the recycling concrete. In addition, the study analyzed the sensitivity of the FS of recycled concrete to input variables, and the results showed that effective water-cement ratio (WC), water absorption of recycling concrete (WAR), and water absorption of natural aggregate (WAN) show more obvious influences on FS, so these factors should be paid more attention in future pavement design using the recycling of concrete. Full article

(This article belongs to the Special Issue Toward Sustainable Development: The Application of Artificial Intelligence in Civil Engineering)

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11 pages, 4891 KiB

Open AccessArticle

A Crack Width Measurement Method of UAV Images Using High-Resolution Algorithms

by Jonghyeon Yoon, Hyunkyu Shin, Mihwa Song, Heungbae Gil and Sanghyo Lee

Sustainability 2023, 15(1), 478; https://doi.org/10.3390/su15010478 - 27 Dec 2022

Cited by 3 | Viewed by 1642

Abstract

The need for maintenance is increasing due to the aging of facilities. In this study, we proposed a crack width measurement method for images collected at safe distances using UAVs (Unmanned Aerial Vehicles). It is a method of measuring the widths of cracks using a high-resolution (VDSR) algorithm, which measures by increasing the resolution of images taken at 3 m intervals on the wall where cracks exist. In addition, the crack width measurement value was compared with a general photographed image and a high-resolution conversion image. As a result, it was confirmed that the crack width measurement of the image to which the high resolution was applied was similar to the actual measured value. These results can help improve the practical applicability of UAVs for facility safety inspections by overcoming the limits of camera resolution and distances between UAVs and facilities introduced in the facility safety inspection. However, more detailed image resolution is required to quantitatively measure the crack width; we intend to improve this through additional studies. Full article

(This article belongs to the Special Issue Toward Sustainable Development: The Application of Artificial Intelligence in Civil Engineering)

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