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A special issue of Infrastructures (ISSN 2412-3811).

Deadline for manuscript submissions: 31 May 2026 | Viewed by 16597

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Special Issue Editors

Prof. Dr. Ahmed A. Ibrahim

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Guest Editor

Department of Civil and Environmental Engineering, University of Idaho, 875 Perimeter Dr. MS 1022, Moscow, ID 83844, USA
Interests: non-linear behavior and modeling; field testing of highway bridges; advanced materials in strengthening of structures; mechanistic damage modeling of concrete
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Mohamed ElGawady

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Guest Editor

Department of Civil Engineering, Missouri University of Science and Technology, Rolla, MO, USA
Interests: seismic behavior of unreinforced masonry (URM) structures; application of fiber reinforced polymers (FRP) in strengthening and repair of masonry/reinforced concrete structures; seismic behavior of reinforced concrete bridges; damage-free bridge columns; segmental construction; rocking mechanics and the use of sustainable materials in seismic prone regions

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is a potent instrument that may assist humans in comprehending and resolving challenging issues. AI offers a special set of tools for innovating infrastructure, enabling quicker, more automated decision-making and better network efficiency. Specifically, AI is able to manage power usage, track changes in traffic patterns, and keep an eye on security thresholds. Large volumes of data can be swiftly processed and analyzed by AI, which allows it to make judgments faster than a human operator could. Cities are becoming safer, more connected, and more efficient thanks to AI-driven infrastructure innovation. In the realm of infrastructure innovation, artificial intelligence (AI) is a priceless tool that may greatly aid cities striving to uphold their status quo and enhance their standard of living. This Special Issue of Advances in Artificial Intelligence for Infrastructures covers a wide range of topics of the application of AI in the structural and transportation engineeering field.

Prof. Dr. Ahmed A. Ibrahim
Prof. Dr. Mohamed ElGawady
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Infrastructures is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

artificial intelligence
deep learning
neural network
infrastructure

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Published Papers (6 papers)

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Research

21 pages, 3762 KB

Open AccessArticle

Multimodal Large Language Models for Visual Attribute Inference in iRAP Road Attribute Coding

by Horia Ameen, Natchapon Jongwiriyanurak, Jesús Balado and Mario Soilan

Infrastructures 2026, 11(3), 95; https://doi.org/10.3390/infrastructures11030095 - 12 Mar 2026

Viewed by 705

Abstract

Road safety assessment is essential for reducing traffic fatalities, with road infrastructure contributing to a substantial proportion of crashes worldwide. International frameworks such as the International Road Assessment Program (iRAP) define standardized attributes for infrastructure auditing; however, many of these attributes remain challenging to automate using imagery alone. This study evaluates V-RoAst (visual question answering for road assessment), a public dataset of road images that are annotated with iRAP-style attributes, using state-of-the-art multimodal large language models (MLLMs), specifically Gemini 2.0 and Gemini 2.5. The analysis focuses on how prompt design influences the accuracy and stability of single image iRAP inference. A token-efficient reduced prompt is developed that preserves the iRAP schema while removing single-class constants, hard-coded administrative fields, and derived or non-visual codes, retaining only visually interpretable attributes. Performance is compared with the original full multi-attribute prompt and single attribute prompts using a fixed evaluation protocol incorporating majority voting, bootstrap 95% confidence intervals, and per-code sample-size checks. Results indicate only minor performance differences between Gemini 2.0 and Gemini 2.5, while prompt optimization produces the most consistent gains, improving macro-F1 scores and tightening confidence intervals for visually grounded attributes such as roadside severity, intersection channelization, and service-road presence. Token analysis shows an approximate 30% reduction in prompt length, reducing computational cost and truncation risk. Overall, the findings demonstrate that prompt scope has a greater impact than model version in image-only iRAP coding, offering practical guidance for scalable infrastructure assessment. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence for Infrastructures)

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37 pages, 48357 KB

Open AccessArticle

Extracting Geometric Parameters of Bridge Cross-Sections from Drawings Using Machine Learning

by Benedikt Faltin, Rosa Alani and Markus König

Infrastructures 2026, 11(2), 48; https://doi.org/10.3390/infrastructures11020048 - 31 Jan 2026

Viewed by 735

Abstract

Bridges are a crucial part of infrastructure, but many are in urgent need of maintenance. Digital methods like Building Information Modeling (BIM) and Digital Twinning can support this process but depend on digital models that are often missing for existing structures. Automating the reconstruction of these models from existing documentation, such as construction drawings, is essential to accelerate digital adoption. Addressing a key step in the reconstruction process, this paper presents an end-to-end pipeline for extracting bridge cross-sections from drawings. First, the YOLOv8 network locates and classifies the cross-sections within the drawing. The results are then processed by the segmentation model Segment Anything Model (SAM), which generates pixel-wise masks without requiring task-specific training data. This eliminates the need for manual mask annotation and enables straightforward adaptation to different cross-section types, making the approach broadly applicable in practice. Finally, a global optimization algorithm fits parametric templates to the masks, minimizing a custom loss function to extract geometric parameters. The pipeline is evaluated on 33 real-world drawings and achieves a median parameter deviation of −2.2 cm and 2.4 cm, with an average standard deviation of 35.4 cm. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence for Infrastructures)

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23 pages, 5438 KB

Open AccessArticle

Exposure Modeling of Transmission Towers for Large-Scale Natural Hazard Risk Assessments Based on Deep-Learning Object Detection Models

by Luigi Cesarini, Rui Figueiredo, Xavier Romão and Mario Martina

Infrastructures 2025, 10(7), 152; https://doi.org/10.3390/infrastructures10070152 - 23 Jun 2025

Viewed by 1784

Abstract

Exposure modeling plays a crucial role in disaster risk assessments by providing geospatial information about assets at risk and their characteristics. Detailed exposure data enhances the spatial representation of a rapidly changing environment, enabling decision-makers to develop effective policies for reducing disaster risk. This work proposes and demonstrates a methodology linking volunteered geographic information from OpenStreetMap (OSM), street-level imagery from Google Street View (GSV), and deep learning object detection models into the automated creation of exposure datasets for power grid transmission towers, assets particularly vulnerable to strong wind, and other perils. Specifically, the methodology is implemented through a start-to-end pipeline that starts from the locations of transmission towers derived from OSM data to obtain GSV images capturing the towers in a given region, based on which their relevant features for risk assessment purposes are determined using two families of object detection models, i.e., single-stage and two-stage detectors. Both models adopted herein, You Only Look Once version 5 (YOLOv5) and Detectron2, achieved high values of mean average precision (mAP) for the identification task (83.67% and 88.64%, respectively), while Detectron2 was found to outperform YOLOv5 for the classification task with a mAP of 64.89% against a 50.62% of the single-stage detector. When applied to a pilot study area in northern Portugal comprising approximately 5.800 towers, the two-stage detector also exhibited higher confidence in its detection on a larger part of the study area, highlighting the potential of the approach for large-scale exposure modeling of transmission towers. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence for Infrastructures)

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19 pages, 2813 KB

Open AccessArticle

Machine Learning-Based Prediction Performance Comparison of Marshall Stability and Flow in Asphalt Mixtures

by Muhammad Farhan Zahoor, Arshad Hussain and Afaq Khattak

Infrastructures 2025, 10(6), 142; https://doi.org/10.3390/infrastructures10060142 - 7 Jun 2025

Cited by 6 | Viewed by 2708

Abstract

The longevity and safety of asphalt pavements, which form the foundation of our transportation infrastructure, are directly impacted by their performance. Pavement performance has traditionally been measured using the Marshall Mix Design method, which is a time- and resource-intensive laboratory procedure. Machine learning algorithms (MLAs) are increasingly popular today and are being utilized in various fields. Their performances vary; therefore, evaluating different MLAs and comparing them is important. The potential of various machine learning (ML) algorithms to predict Marshall Stability (MS) and Marshall Flow (MF) was investigated in this work. We collected data from published studies in the literature encompassing 732 data points to train and evaluate ML models. Eight key input parameters were considered for modeling. We used three feature importance analysis techniques (Random Forest, Permutation Importance, and Lasso Regression) to determine which parameters were the most significant. Linear regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machines (SVMs), Gradient Boosting Machines (GBMs), and Artificial Neural Networks (ANNs) were the six MLAs that were assessed. Robust statistical measures such as MSE, MAE, R², and RMSE were employed to evaluate each model’s performance. Our results indicate that the RF algorithm had the best performance for both MS and MF parameter prediction, followed by ANN and DT. The predicted and actual values showed a strong correlation, which was evidenced by the high R² and the lowest values in other error metrics, indicating good performance. This highlights the significance of selecting an optimal machine learning algorithm for a particular predictive task. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence for Infrastructures)

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29 pages, 1565 KB

Open AccessArticle

Analyzing High-Speed Rail’s Transformative Impact on Public Transport in Thailand Using Machine Learning

by Chinnakrit Banyong, Natthaporn Hantanong, Panuwat Wisutwattanasak, Thanapong Champahom, Kestsirin Theerathitichaipa, Rattanaporn Kasemsri, Manlika Seefong, Vatanavongs Ratanavaraha and Sajjakaj Jomnonkwao

Infrastructures 2025, 10(3), 57; https://doi.org/10.3390/infrastructures10030057 - 10 Mar 2025

Cited by 6 | Viewed by 5198

Abstract

This study investigates the impact of high-speed rail (HSR) on Thailand’s public transportation market and evaluates the effectiveness of machine learning techniques in predicting travel mode choices. A stated preference survey was conducted with 3200 respondents across 16 provinces, simulating travel scenarios involving buses, trains, airplanes, and HSR. The dataset, consisting of 38,400 observations, was analyzed using the CatBoost model and the multinomial logit (MNL) model. CatBoost demonstrated superior predictive performance, achieving an accuracy of 0.853 and an AUC of 0.948, compared to MNL’s accuracy of 0.749 and AUC of 0.879. Shapley additive explanations (SHAP) analysis identified key factors influencing travel behavior, including cost, service frequency, waiting time, travel time, and station access time. The results predict that HSR will capture 88.91% of the intercity travel market, significantly reducing market shares for buses (4.76%), trains (5.11%), and airplanes (1.22%). The findings highlight the transformative role of HSR in reshaping travel patterns and offer policy insights for optimizing pricing, service frequency, and accessibility. Machine learning enhances predictive accuracy and enables a deeper understanding of mode choice behavior, providing a robust analytical framework for transportation planning. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence for Infrastructures)

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18 pages, 5677 KB

Open AccessArticle

Computer Vision-Based Concrete Crack Identification Using MobileNetV2 Neural Network and Adaptive Thresholding

by Li Hui, Ahmed Ibrahim and Riyadh Hindi

Infrastructures 2025, 10(2), 42; https://doi.org/10.3390/infrastructures10020042 - 18 Feb 2025

Cited by 9 | Viewed by 4455

Abstract

Concrete is widely used in different types of buildings and bridges; however, one of the major issues for concrete structures is crack formation and propagation during its service life. These cracks can potentially introduce harmful agents into concrete, resulting in a reduction in the overall lifespan of concrete structures. Traditional methods for crack detection primarily hinge on manual visual inspection, which relies on the experience and expertise of inspectors using tools such as magnifying glasses and microscopes. To address this issue, computer vision is one of the most innovative solutions for concrete cracking evaluation, and its application has been an area of research interest in the past few years. This study focuses on the utilization of the lightweight MobileNetV2 neural network for concrete crack detection. A dataset including 40,000 images was adopted and preprocessed using various thresholding techniques, of which adaptive thresholding was selected for developing the crack evaluation algorithm. While both the convolutional neural network (CNN) and MobileNetV2 indicated comparable accuracy levels in crack detection, the MobileNetV2 model’s significantly smaller size makes it a more efficient selection for crack detection using mobile devices. In addition, an advanced algorithm was developed to detect cracks and evaluate crack widths in high-resolution images. The effectiveness and reliability of both the selected method and the developed algorithm were subsequently assessed through experimental validation. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence for Infrastructures)

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