AI in civil engineering最新文献

{"title":"Predicting post-wildfire debris flow onset using machine learning models on multi-parameter experimental data","authors":"Mahta Movasat,&nbsp;Ingrid Tomac","doi":"10.1007/s43503-026-00094-w","DOIUrl":"10.1007/s43503-026-00094-w","url":null,"abstract":"<div><p>The increasing frequency of wildfires, particularly in wildland-urban interface zones, has elevated the hazard posed by post-wildfire debris flows. Following combustion, surface and near-surface soils often become hydrophobic, a phenomenon that occurs predominantly on sand-based hillslopes. Rainwater and eroded soil accumulate downslope, frequently evolving into destructive debris flows. Soil hydrophobicity exacerbates erosion, distinguishing post-wildfire debris flows from natural debris flows in terms of intensity, duration, and destructive potential. Therefore, understanding the timing and conditions under which such debris flows initiate is critical. This initiation results from coupled effects among several key parameters: rainfall intensity (RI), slope gradient (δ), water entry value (<span>({Psi }_{text{wev}})</span>), and grain size (<i>D</i>₅₀). Machine Learning (ML) techniques have become increasingly valuable in geotechnical engineering due to their ability to model complex systems without predefined assumptions. This study applied multiple ML algorithms: multiple linear regression (MLR), logistic regression (LR), support vector classifier (SVC), K-means clustering, and principal component analysis (PCA) to predict and classify outcomes from laboratory experiments that model field conditions using a rain device on various soils in sloped flumes. The results indicate that while MLR performs adequately in predicting total discharge, its accuracy in predicting erosion is limited, particularly for coarse sand. In contrast, LR and SVC achieve satisfactory accuracy in classifying failure outcomes, supported by clustering and dimensionality reduction techniques. Sensitivity analysis reveals that fine sand is highly susceptible to erosion, especially under low-intensity, prolonged rainfall. Furthermore, the first ten minutes of high-intensity rainfall emerge as the most critical period for discharge generation and slope failure. Collectively, these findings underscore the potential of machine learning to enhance post-wildfire hazard assessment and inform emergency response planning.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-026-00094-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147830125","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":"Metaheuristic-based broad learning systems for compressive strength prediction of concrete structures","authors":"Sarat Chandra Nayak,&nbsp;Sanjib Kumar Nayak","doi":"10.1007/s43503-026-00093-x","DOIUrl":"10.1007/s43503-026-00093-x","url":null,"abstract":"<div><p>The Broad Learning System (BLS) provides an effective framework for nonlinear mapping, offering advantages over traditional deep neural networks through its expanded input node architecture. While BLS has demonstrated improved classification accuracy and reduced computational cost, its performance can be compromised by randomly initialized input weights and biases. To address this limitation, this study proposes an integration of metaheuristic optimization algorithms with BLS (termed MBLS). Five metaphor-free optimization methods and four algorithm-specific, parameter-free methods are independently employed to optimize BLS parameters, yielding nine hybrid models. These models are applied to four benchmark datasets for predicting the compressive strength (CS) of concrete structures. Although various machine learning (ML) and deep learning (DL) methods have been explored for this task, their practical utility is constrained by structural and computational complexity. In contrast, the proposed MBLS framework achieves both structural simplicity and computational efficiency. The predictive performance of the nine hybrid MBLS models, along with a multilayer perceptron artificial neural network (MLPANN), is evaluated on four real-world datasets using four performance metrics: mean absolute percentage error (MAPE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (<i>R</i>²). To further enhance prediction accuracy, the training data are augmented with interpolated samples. Extensive experimental results, comparative analyses, and statistical tests confirm the effectiveness of the MBLS methods. Among them, the BL-BMR model consistently achieves the best overall performance, evidenced by the lowest average MAPE, RMSE, and MAE, and the highest <i>R</i>² across datasets. Specifically, adopting BL-BMR forecasts yields MAPE improvements ranging from 8% to 84.98% for Dataset 1, 57.50% to 78.55% for Dataset 2, 7.82% to 62.74% for Dataset 3, and 27.24% to 42.76% for Dataset 4. The strong nonlinear input–output mapping capability of BLS, combined with effective parameter search via the BMR algorithm, renders the hybrid model highly effective for precise CS prediction.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-026-00093-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147796896","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":"Correction: Predictive modeling of punchouts in continuously reinforced concrete pavement: a machine learning approach","authors":"Ghazi Al-Khateeb,&nbsp;Ali Alnaqbi,&nbsp;Waleed Zeiada","doi":"10.1007/s43503-026-00096-8","DOIUrl":"10.1007/s43503-026-00096-8","url":null,"abstract":"","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-026-00096-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147797220","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":"Novel computer vision algorithm for accurate multi-label classification of concrete bridge defects","authors":"Jamiu Lateef,&nbsp;Xiong Yu","doi":"10.1007/s43503-026-00095-9","DOIUrl":"10.1007/s43503-026-00095-9","url":null,"abstract":"<div><p>Maintaining the structural integrity of reinforced concrete bridges necessitates the timely and accurate detection of surface defects. Conventional inspection methodologies remain labor-intensive, inherently subjective, and susceptible to human error, driving the need for automated assessment frameworks. This study introduces a multi-label defect classification model tailored for reinforced concrete bridge inspection, engineered to process imagery consistent with prevailing bridge inspection standards. The proposed framework is designed to simultaneously identify multiple co-occurring defects within a single image, addressing the practical reality of overlapping deterioration mechanisms. Leveraging the open-source Concrete Defect Bridge Image Dataset (CODEBRIM), three distinct ImageNet-pretrained deep neural network architectures were subjected to systematic hyperparameter optimization and fine-tuning to enhance classification performance across bridge-relevant defect categories. Beyond achieving high per-class accuracy, the optimized model attained a subset accuracy of 84.0% and a micro-averaged F1-score of 85.2% on a held-out test set, signifying robust recognition of overlapping distress conditions. Furthermore, evaluation on a synthetically generated dataset validated the model's generalization capacity under domain shift. The findings demonstrate that the proposed framework effectively supports automated defect documentation and holds significant potential for enhancing the objectivity and efficiency of bridge condition assessment protocols.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-026-00095-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147737841","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 novel GRU–TimeMixerKAN hybrid model for robust landslide displacement forecasting: a case study of the Three Gorges Reservoir Area","authors":"Bingheng Li,&nbsp;Hong Zheng,&nbsp;Xun Zhang","doi":"10.1007/s43503-026-00091-z","DOIUrl":"10.1007/s43503-026-00091-z","url":null,"abstract":"<div><p>Landslides, as prevalent geohazards, exhibit complex and nonlinear evolutionary dynamics, frequently triggered by the coupled effects of reservoir water-level fluctuations and extreme precipitation. Such events are often characterized by abrupt, step-like deformation, posing significant challenges for accurate long-term displacement forecasting. To address the limitations of conventional models, including poor generalization, low robustness to chaotic disturbances, and insufficient capacity for nonlinear representation, we propose a hybrid deep learning framework termed GRU–TimeMixerKAN. This model synergistically integrates the sequential modeling capabilities of Gated Recurrent Units (GRU), the temporal-feature decoupling mechanism of TimeMixer, and the high-order nonlinear approximation power of the Kolmogorov–Arnold Network (KAN). Enhancements such as differencing-based detrending, sliding-window sampling, and automated hyperparameter optimization via Optuna are incorporated to further refine performance. The efficacy of the proposed model is evaluated using long-term displacement monitoring data from three reactivated reservoir landslides in the Three Gorges Reservoir Area (TGRA), with its performance benchmarked against nine state-of-the-art deep learning baselines. The results demonstrate that GRU–TimeMixerKAN consistently achieves the lowest Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), alongside competitive Symmetric Mean Absolute Percentage Error (sMAPE) and the highest Coefficient of Determination (R²). These findings underscore its superior capability in capturing displacement trends, responding to sudden changes, and generalizing robustly across diverse landslide cases. This study presents an effective and scalable methodology for advancing intelligent early warning and prediction systems for landslides.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-026-00091-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147642656","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":"Prediction of damage signals at inaccessible locations using a machine learning approach","authors":"Anoop Sharma,&nbsp;Neetika Saha,&nbsp;Pijush Topdar","doi":"10.1007/s43503-026-00090-0","DOIUrl":"10.1007/s43503-026-00090-0","url":null,"abstract":"<div><p>Structures are prone to damage. Identification and localization of damage at its initiation stage are extremely helpful for ensuring safety, economy, and operational benefits. A machine learning (ML) approach is helpful for this purpose, provided that high-quality and sufficient damage signals are available from a variety of locations across the structure. Such signals, generated at damage initiation, are frequently obtained using a non-destructive testing (NDT) technique, such as acoustic emission (AE), which employs the pencil lead break (PLB) method. However, PLB is not possible at inaccessible locations of the structure. Therefore, synthetic experimental signals are required for such locations. Accordingly, the present study aims to generate synthetic experimental signals from numerically simulated AE signals using an artificial neural network (ANN). Here, parameters from numerical signals serve as inputs, and the corresponding parameters of experimental signals are outputs. The most relevant signal parameters are determined using the Pearson correlation coefficient (PCC). The developed model is found to perform very well, achieving an accuracy of around 99%.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-026-00090-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147606626","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":"Ensemble learning-based strength prediction and model interpretability analysis of engineered cementitious composites","authors":"Yufei Wang,&nbsp;Junbo Sun,&nbsp;Xianda Liu,&nbsp;Yimeng Huang,&nbsp;Xiangyu Wang,&nbsp;Li Zuo,&nbsp;Dong Wang","doi":"10.1007/s43503-026-00089-7","DOIUrl":"10.1007/s43503-026-00089-7","url":null,"abstract":"<div><p>Accurate prediction of compressive strength is essential for improving the performance and durability of Engineered Cementitious Composites (ECC) in construction applications. Traditional methods often fall short in accounting for the complex interactions between material properties, such as fiber type, matrix composition, and curing conditions. To address this challenge, this study presents an advanced ensemble learning framework based on a dataset of 313 ECC samples characterized by 18 key features. The ensemble model integrates three base learners, namely Random Forest (RF), Gradient Boosting Decision Trees (GBDT), and Support Vector Regression (SVR), along with a meta-learner selected from ten candidate models. The proposed ensemble model demonstrates significantly higher prediction accuracy compared to conventional approaches. The results show that the ensemble model achieves a coefficient of determination (<i>R</i>²) of 0.896, a root mean square error (RMSE) of 5.734, and a mean absolute error (MAE) of 4.505, substantially outperforming individual models. Among the evaluated meta-learners, Lasso Regression was identified as the optimal choice. Its regularization capability effectively mitigated overfitting and enhanced generalization, leading to a notable improvement in the final predictive performance of the stacking framework. Furthermore, SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDP) were employed for model interpretability and visualization. The analysis reveals that factors such as fiber elastic modulus, silica fume content, and fiber volume fraction significantly contribute to the enhancement of ECC compressive strength. This model provides practical insights for optimizing the design and application of ECC materials.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-026-00089-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147560433","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":"Machine learning-based prediction of the structural performance of a long-bolted steel connection","authors":"Mostafa Elhadary,&nbsp;Ahmed Bediwy,&nbsp;Ahmed Elshaer","doi":"10.1007/s43503-026-00087-9","DOIUrl":"10.1007/s43503-026-00087-9","url":null,"abstract":"<div><p>In response to the housing shortage in Canada, particularly in northern and remote communities, modular houses have emerged as a viable solution. These prefabricated structures offer speed, cost-efficiency, and flexibility. To enhance the durability and functionality of these modular homes, innovative construction techniques are being explored. A new bolted connection, utilizing high-strength long bolts, has been introduced for hollow structural sections (HSS), which can be designed using regression models trained by an experimentally validated finite element model (FEM). This study employs machine learning techniques, including neural networks, genetic regression, and decision trees, to detect the failure mode and predict the ultimate moment capacity of HSS moment connections under monotonic loading. A nonlinear validated FEM was developed using LS-DYNA software, and a matrix of 240 FEMs was generated to train and test the machine learning models, including a range of various design parameters such as the extended plate thickness, number of bolts, bolt arrangement, and bolt diameter. Five machine learning algorithms were used for classification and regression learning, with hyperparameter optimization applied to enhance their accuracy. Mathematical formulas for predicting the ultimate moment capacity were developed using genetic algorithm-based symbolic regression, trained on 70% of the matrix parameters. These formulas were then validated and tested with the remaining 30%, demonstrating high accuracy. Findings illustrate the efficiency of machine learning approaches for precisely predicting the ultimate capacity and failure patterns of bolted connections, highlighting their promise as reliable tools in design, complementing both experimental and analytical methods.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-026-00087-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147559342","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":"Large language model-based multi-agent systems for automated foundation design: router-driven task classification and expert selection framework","authors":"Sompote Youwai,&nbsp;David Phim,&nbsp;Vianne Gayl Murcia,&nbsp;Rianne Clair Onas","doi":"10.1007/s43503-026-00088-8","DOIUrl":"10.1007/s43503-026-00088-8","url":null,"abstract":"<div><p>This preliminary study introduces and evaluates a router-based multi-agent framework for automated foundation design calculations through intelligent task classification and expert selection. Three configurations were assessed: single-agent processing, multi-agent designer-checker architecture, and router-based expert selection, using baseline models including DeepSeek R1, ChatGPT 4 Turbo, Grok 3, and Gemini 2.5 Pro. Initial evaluation on 27 test cases with triple-trial execution shows promising performance: the router-based system achieved 95.00% for shallow foundations and 90.63% for pile design, representing improvements of 8.75 and 3.13 percentage points over standalone Grok 3, respectively, and outperforming conventional workflows by 10.0–43.75 percentage points. Grok 3 demonstrated superior standalone performance, indicating enhanced large language model (LLM) mathematical reasoning capabilities. The dual-tier classification framework successfully distinguished foundation types, enabling appropriate analytical approaches. While these preliminary results suggest router-based multi-agent systems as a promising approach for foundation design automation, the limited sample size necessitates comprehensive validation on larger, more diverse datasets before deployment recommendations. Safety–critical requirements necessitate continued human oversight in professional applications. This work provides a methodological foundation for future research in AI-assisted geotechnical engineering.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-026-00088-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147336101","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":"Explainable AI for CO2 emissions reduction in housing manufacturing: a review","authors":"Miguel Mora,&nbsp;Pingbo Tang","doi":"10.1007/s43503-026-00086-w","DOIUrl":"10.1007/s43503-026-00086-w","url":null,"abstract":"<div><p>The construction industry faces the challenge of decarbonization. Integrating manufacturing principles and Artificial Intelligence (AI) offers a promising pathway to reduce CO₂ emissions, specifically by integrating CO₂-emission variables into AI-driven production schedules. However, transparency to users is essential, as human users remain ultimately responsible for production outcomes. This requirement can be met through Explainable AI (XAI), which aims to provide transparency for end users. However, defining an appropriate XAI approach requires understanding problem- and industry-specific variables. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, this study examines the state of the art in XAI literature to identify research gaps and formulate actionable recommendations. The study provides insights for developing an XAI approach to support the decarbonization of housing manufacturing explicitly. The key findings highlight the need for user-centric and industry-specific frameworks and the importance of clearly defining the XAI-AI relationship. Finally, this research synthesizes these findings into a roadmap to guide future research on XAI for the decarbonization of housing manufacturing.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-026-00086-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147338631","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}