AI in civil engineering最新文献

{"title":"AI-driven spectral analysis of soil heaving for automated surveys in rail transport infrastructure","authors":"Artem Zaitsev,&nbsp;Andrey Koshurnikov,&nbsp;Vladimir Gagarin,&nbsp;Denis Frolov,&nbsp;German Rzhanitsyn","doi":"10.1007/s43503-025-00072-8","DOIUrl":"10.1007/s43503-025-00072-8","url":null,"abstract":"<div><p>The expansion of rail transport infrastructures necessitates accurate and efficient soil surveys to ensure long-term stability and performance, particularly in regions prone to soil heaving. This study aimed to demonstrate the potential of non-destructive spectral analysis combined with Agentic Artificial Intelligence for automating the identification of soil heaving potential, providing a transformative approach to soil assessment in railway construction. A robust AI-agent was developed to predict soil heaving potential across temperature regimes (ranging from 0°C to -5°C and back), enabling characterization of the relative acoustic compressibility coefficient (β) based on the physical and mechanical properties of the soil. The main objective was to develop a framework that integrated spectral reflectance data with machine learning algorithms to predict soil heaving potential and reduce the reliance on traditional invasive methods. The experimental setup employed digital techniques to process and record longitudinal and transverse acoustic pulse signals reflected from piezoelectric sensors mounted on soil specimens. The processed signals were automatically transferred via a USB adapter to a PC for further analysis by the AI-agent. Acoustic diagnostics of the soils were performed using Fast-Fourier Transform (FFT) Spectral Analysis, followed by correlation of waveform spectra with heaving deformation. The AI-agent utilized a hybrid architecture combining Convolutional Neural Network (CNN), Support Vector Machine (SVM), and Random Forest (RF) algorithms to address the complexities of heterogeneous soil data and multifaceted prediction tasks—including heaving classification and deformation regression—while mitigating overfitting. Soil heaving potential was accurately predicted by the AI agent, with minor variations attributed to equipment sensitivity. </p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-025-00072-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145256442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictive modeling of concrete arch dam behavior: evaluating the efficacy of Random Forest and Radial Basis Function Networks","authors":"A. M. Babadi,&nbsp;H. Mirzabozorg,&nbsp;K. Baharan","doi":"10.1007/s43503-025-00071-9","DOIUrl":"10.1007/s43503-025-00071-9","url":null,"abstract":"<div><p>This study investigates the application of established open-source machine learning tools, specifically CatBoost, XGBoost, LightGBM, and TensorFlow, which are based on Forest and Radial Basis Function Networks, to predict and analyze the structural behavior of concrete arch dams. Utilizing the Karun-I dam as a case study, the research assesses the performance of various machine learning frameworks. The results demonstrate that Random Forest-based methods achieve superior prediction accuracy and computational efficiency in comparison to Radial Basis Function Networks. Additionally, the analysis emphasizes the critical influence of lake levels as the primary factor impacting dam displacement, as revealed through feature importance evaluation. Overall, this research underscores the promising potential of machine learning in enhancing structural health monitoring for large dams, offering significant insights that contribute to the improvement of safety measures and operational efficiency in dam management.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-025-00071-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bridging AI and explainability in civil engineering: the Yin-Yang of predictive power and interpretability","authors":"Monjurul Hasan,&nbsp;Ming Lu","doi":"10.1007/s43503-025-00066-6","DOIUrl":"10.1007/s43503-025-00066-6","url":null,"abstract":"<div><p>Civil engineering relies on data from experiments or simulations to calibrate models that approximate system behaviors. This paper examines machine learning (ML) algorithms for AI-driven decision support in civil engineering, specifically construction engineering and management, where complex input–output relationships demand both predictive accuracy and interpretability. Explainable AI (XAI) is critical for safety and compliance-sensitive applications, ensuring transparency in AI decisions. The literature review identifies key XAI evaluation attributes—model type, explainability, perspective, and interpretability and assesses the Enhanced Model Tree (EMT), a novel method demonstrating strong potential for civil engineering applications compared to commonly applied ML algorithms. The study highlights the need to balance AI’s predictive power with XAI’s transparency, akin to the Yin–Yang philosophy: AI advances in efficiency and optimization, while XAI provides logical reasoning behind conclusions. Drawing on insights from the literature, the study proposes a tailored XAI assessment framework addressing civil engineering's unique needs—problem context, data constraints, and model explainability. By formalizing this synergy, the research fosters trust in AI systems, enabling safer and more socially responsible outcomes. The findings underscore XAI’s role in bridging the gap between complex AI models and end-user accountability, ensuring AI’s full potential is realized in the field.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-025-00066-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145007843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Few-shot meta-learning for concrete strength prediction: a model-agnostic approach with SHAP analysis","authors":"Mayaz Uddin Gazi,&nbsp;Md. Titumir Hasan,&nbsp;Ponkaj Debnath","doi":"10.1007/s43503-025-00064-8","DOIUrl":"10.1007/s43503-025-00064-8","url":null,"abstract":"<div><p>Predicting concrete compressive strength with limited data remains a critical challenge in civil engineering. This study proposes a novel framework integrating Model-Agnostic Meta-Learning (MAML) with SHAP (Shapley Additive Explanations) to improve predictive accuracy and interpretability in data-scarce scenarios. Unlike conventional machine learning models that require extensive data, the MAML-based approach enables rapid adaptation to new tasks using minimal samples, offering robust generalization in few-shot learning contexts. The proposed pipeline includes structured preprocessing, normalization, a neural network-based meta-learning core, and SHAP-based feature attribution. A curated dataset of 430 samples was used, focusing on 28-day compressive strength, with input features including cement, water, aggregates, admixtures, and age. Compared to standard models like XGBoost and Random Forest, the MAML framework achieved superior performance, with MAE = 3.56 MPa, RMSE = 5.55 MPa, and R² = 0.913. SHAP analysis revealed nonlinear interactions and dominant factors like water-cement ratio, curing age, and aggregate content. Statistical validation via the Wilcoxon Signed-Rank Test confirmed the significance of the model’s improvements (p &lt; 0.05). Furthermore, SHAP insights closely align with domain knowledge and mix design principles, enhancing model transparency for practical application. This work demonstrates the applicability of meta-learning in civil engineering and provides a scalable, interpretable solution for strength prediction in real-world, data-limited conditions. </p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-025-00064-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144923320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stochastic artificial intelligence models for water resources management: innovative riverflow estimation amidst uncertainty","authors":"Mojtaba Poursaeid","doi":"10.1007/s43503-025-00062-w","DOIUrl":"10.1007/s43503-025-00062-w","url":null,"abstract":"<div><p>Rivers provide irreplaceable resources for human life, and the problem of water scarcity has attracted serious attention worldwide. In this study, Kashkan River located in Loristan Province of Iran was studied using data obtained from the database of Iran Water Resources Company (IWRC). Three distinct machine learning (ML) models – Regression Tree (RT), Random Search Regression Tree (RSRT), and Bayesian Optimization Regression Tree (BORT) – were utilized to enhance water resource management practices. The primary model used was RT, a method that uses Bayesian optimization and stochastic search algorithms to provide an accurate estimate of the maximum flow within a river. The two hybrid models, RSRT and BORT, were introduced to improve the model performance. Through a comprehensive comparison and analysis of the results generated by these models, valuable insights were gained. Among the three models, the RSRT model demonstrated superior performance and accuracy metrics in streamflow (SF) modeling, closely aligning its results with a DR line of 1, indicating an optimal fit. The BORT and RT models also achieved excellent results, with their performance being on par with that of the top-performing RSRT model.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-025-00062-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145163594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semantic and lexical analysis of pre-trained vision language artificial intelligence models for automated image descriptions in civil engineering","authors":"Pedram Bazrafshan,&nbsp;Kris Melag,&nbsp;Arvin Ebrahimkhanlou","doi":"10.1007/s43503-025-00063-9","DOIUrl":"10.1007/s43503-025-00063-9","url":null,"abstract":"<div><p>This paper investigates the application of pre-trained Vision-Language Models (VLMs) for describing images from civil engineering materials and construction sites, with a focus on construction components, structural elements, and materials. The novelty of this study lies in the investigation of VLMs for this specialized domain, which has not been previously addressed. As a case study, the paper evaluates ChatGPT-4v’s ability to serve as a descriptor tool by comparing its performance with three human descriptions (a civil engineer and two engineering interns). The contributions of this work include adapting a pre-trained VLM to civil engineering applications without additional fine-tuning and benchmarking its performance using both semantic similarity analysis (SentenceTransformers) and lexical similarity methods. Utilizing two datasets—one from a publicly available online repository and another manually collected by the authors—the study employs whole-text and sentence pair-wise similarity analyses to assess the model’s alignment with human descriptions. Results demonstrate that the best-performing model achieved an average similarity of 76% (4% standard deviation) when compared to human-generated descriptions. The analysis also reveals better performance on the publicly available dataset.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-025-00063-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A transferable machine learning model for real-time forecast of epidemic dynamics and pre-trigger event warning","authors":"Enpei Chen,&nbsp;Xiong Yu","doi":"10.1007/s43503-025-00059-5","DOIUrl":"10.1007/s43503-025-00059-5","url":null,"abstract":"<div><p>Wastewater-based epidemiology (WBE) is emerging as an effective tool to provide early warnings of potential disease outbreaks within communities through detecting the presence of pathogens in wastewater before clinical cases are reported. Nevertheless, quantitative prediction of future clinical case is challenging as uncertainties of dynamic shedding and disease transmission patterns can lead to complex correlation between wastewater viral concentration and clinical cases. Such complexities, augmented by factors such as viral variant, public behavioral change, etc., make it challenging to develop empirical models or data-driven models to provide accurate prediction of disease case for public health policy makings. To address this gap, this study developed an iterative data-driven framework utilizing Long-Short Time Memory (LSTM) neural networks for multi-timestep real-time predictions of future clinical cases based on WBE. The proposed LSTM model structure integrates both wastewater and historical clinical data as inputs. The prediction framework enables the update of LSTM model as more WBE dataset become available to enhance its adaptability to evolving pandemic stages. This framework was applied for real-time forecasting of COVID-19 clinical cases based on dataset of Ohio Wastewater Monitoring Project from July 2020 to October 2023. The developed iterative LSTM models were proven to achieve excellent performance in making clinical case predictions at different stages of COVID-19 pandemic. Early warning threshold of viral surge was defined by moving percentile method and results showed that the model achieved over 90% accuracy in future clinical case prediction and therefore demonstrated high reliability in pre-warning of potential disease outbreaks. This framework was also found to possess strong transferability across diverse geographic regions. The impacts of social policies and events on model predictions as well as the ramification of this model for future pandemics warning are discussed.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-025-00059-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145162979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized machine learning algorithms with SHAP analysis for predicting compressive strength in high-performance concrete","authors":"Samuel Olaoluwa Abioye,&nbsp;Yusuf Olawale Babatunde,&nbsp;Oluwafikejimi Abigail Abikoye,&nbsp;Aisha Nene Shaibu,&nbsp;Bailey Jonathan Bankole","doi":"10.1007/s43503-025-00061-x","DOIUrl":"10.1007/s43503-025-00061-x","url":null,"abstract":"<div><p>This research examines the application of eight different machine learning (ML) algorithms for predicting the compressive strength of high-performance concrete (HPC). Achieving precise predictions is crucial for enhancing structural reliability and optimizing resource usage in construction projects. The analysis utilized the “Concrete Compressive Strength” dataset, sourced from UC Irvine’s publicly available ML repository. The models evaluated include Gradient Boosting Regressor (GBR), Extreme Gradient Boosting Regression (XGBoost), Random Forest (RF), Support Vector Regression (SVR), Artificial Neural Network (ANN), Multilayer Perceptron (MLP), Lasso, and k-Nearest Neighbors (KNN). To enhance performance, critical data preprocessing steps were undertaken, which involved feature scaling, cleaning, and normalization. Hyperparameter tuning via Grid Search (GS) and K-fold cross-validation further optimized the models. Among those analyzed, XGBoost and GBR achieved the highest predictive accuracy, with R² values of 93.49% and 92.09% respectively, coupled with lower mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE). SHapley Additive exPlanations (SHAP) analysis revealed cement content and curing age as the most significant factors affecting compressive strength. Validation against experimental data confirmed the reliability of XGBoost and GBR through consistent prediction patterns and close alignment with empirical measurements. The results establish ML as an effective approach for HPC strength prediction, offering advantages in computational efficiency and accuracy over conventional analytical methods.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-025-00061-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilizing large language models for semantic enrichment of infrastructure condition data: a comparative study of GPT and Llama models","authors":"Lea Höltgen,&nbsp;Sven Zentgraf,&nbsp;Philipp Hagedorn,&nbsp;Markus König","doi":"10.1007/s43503-025-00055-9","DOIUrl":"10.1007/s43503-025-00055-9","url":null,"abstract":"<div><p>Relational databases containing construction-related data are widely used in the Architecture, Engineering, and Construction (AEC) industry to manage diverse datasets, including project management and building-specific information. This study explores the use of large language models (LLMs) to convert construction data from relational databases into formal semantic representations, such as the resource description framework (RDF). Transforming this data into RDF-encoded knowledge graphs enhances interoperability and enables advanced querying capabilities. However, existing methods like R2RML and Direct Mapping face significant challenges, including the need for domain expertise and scalability issues. LLMs, with their advanced natural language processing capabilities, offer a promising solution by automating the conversion process, reducing the reliance on expert knowledge, and semantically enriching data through appropriate ontologies. This paper evaluates the potential of four LLMs (two versions of GPT and Llama) to enhance data enrichment workflows in the construction industry and examines the limitations of applying these models to large-scale datasets.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-025-00055-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145164139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An enhanced method of CNNs by incorporating the clustering-guided block for concrete crack recognition","authors":"Hui Li,&nbsp;Chenyu Liu,&nbsp;Ning Zhang,&nbsp;Wei Shi","doi":"10.1007/s43503-025-00058-6","DOIUrl":"10.1007/s43503-025-00058-6","url":null,"abstract":"<div><p>Concrete cracking poses a significant threat to the safety and stability of crucial infrastructure such as bridges, roads, and building structures. Recognizing and accurately measuring the morphology of cracks is essential for assessing the structural integrity of these elements. This paper introduces a novel Crack Segmentation method known as CG-CNNs, which combines a Clustering-guided (CG) block with a Convolutional Neural Network (CNN). The innovative CG block operates by categorizing extracted image features into K groups, merging these features, and then simultaneously feeding the augmented features and original image into the CNN for precise crack image segmentation. It automatically determines the optimal K value by evaluating the Silhouette Coefficient for various K values, utilizing the grayscale feature value of each cluster centroid as a defining characteristic for each category. To bolster our approach, we curated a dataset of 2500 crack images from concrete structures, employing rigorous pre-processing and data augmentation techniques. We benchmarked our method against three prevalent CNN architectures: DeepLabV3 + , U-Net, and SegNet, each augmented with the CG block. An algorithm specialized for assessing crack edge recognition accuracy was employed to analyze the proposed method's performance. The comparative analysis demonstrated that CNNs enhanced with the CG block exhibited exceptional crack image recognition capabilities and enabled precise segmentation of crack edges. Further investigation revealed that the CG-DeepLabV3 + model excelled, achieving an F1 score of 0.90 and an impressive intersection over union (IoU) value of 0.82. Notably, the CG-DeepLabV3 + model significantly reduced the recognition error for locating crack edges to a mere 2.31 pixels. These enhancements mark a significant advancement in developing accurate algorithms based on deep neural networks for identifying concrete crack edges reliably. In conclusion, our CG-CNNs approach offers a highly accurate method for crack segmentation, which is invaluable for machine-based measurements of cracks on concrete surfaces.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-025-00058-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145161595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}