AI in civil engineering最新文献

{"title":"Extensible portal frame bridge synthetic dataset for structural semantic segmentation","authors":"Tatiana Fountoukidou,&nbsp;Iuliia Tkachenko,&nbsp;Benjamin Poli,&nbsp;Serge Miguet","doi":"10.1007/s43503-024-00041-7","DOIUrl":"10.1007/s43503-024-00041-7","url":null,"abstract":"<div><p>A number of bridges have collapsed around the world over the past years, with detrimental consequences on safety and traffic. To a large extend, such failures can be prevented by regular bridge inspections and maintenance, tasks that fall in the general category of structural health monitoring (SHM). Those procedures are time and labor consuming, which partly accounts for their neglect. Computer vision and artificial intelligence (AI) methods have the potential to ease this burden, by fully or partially automating bridge monitoring. A critical step in this automation is the identification of a bridge’s structural components. In this work, we propose an extensible synthetic dataset for structural component semantic segmentation of portal frame bridges (<b>PFBridge</b>). We first create a 3 dimensional (3D) generic mesh representing the bridge geometry, while respecting a set of rules. The definition of new, or the extension of the existing rules can adjust the dataset to specific needs. We then add textures and other realistic elements to the model, and create an automatically annotated synthetic dataset. The synthetic dataset is used in order to train a deep semantic segmentation model to identify bridge components on bridge images. The amount of available real images is not sufficient to entirely train such a model, but is used to refined the model trained on the synthetic data. We evaluate the contribution of the dataset to semantic segmentation by training several segmentation models on almost 2,000 synthetic images and then finetuning with 88 real images. The results show an increase of <b>28%</b> on the F1-score when the synthetic dataset is used. To demonstrate a potential use case, the model is integrated in a 3D point cloud capturing system, producing an annotated point cloud where each point is associated with a semantic category (structural component). Such a point cloud can then be used in order to facilitate the generation of a bridge’s digital twin.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-024-00041-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142826102","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 compressive strength of nano silica and micro silica from rice husk ash using multivariate regression models","authors":"Mustapha A. Raji,&nbsp;Boluwatife M. Falola,&nbsp;Jesse T. Enikuomehin,&nbsp;Akintoye O. Oyelade,&nbsp;Yetunde O. Abiodun,&nbsp;Yusuf A. Olaniyi,&nbsp;Olusola G. Olagunju,&nbsp;Kosisochukwu L. Anyaegbuna,&nbsp;Musa O. Abdulkareem,&nbsp;Christopher A. Fapohunda","doi":"10.1007/s43503-024-00043-5","DOIUrl":"10.1007/s43503-024-00043-5","url":null,"abstract":"<div><p>The use of agricultural by-products, such as Rice Husk Ash (RHA), in concrete production has gained significant attention as a sustainable alternative to traditional construction materials. This study aims to evaluate and compare the effects of Nano-Rice Husk Ash (NRHA) and Micro-Rice Husk Ash (MRHA) on the compressive strength of concrete. Concrete samples were prepared with varying replacement levels of NRHA (0% to 3%) and MRHA (0% to 14%) and underwent thorough examination through both slump and compressive strength tests conducted at 7, 21, 28, and 56 days. The results showed that NRHA achieved maximum compressive strength at a 1% replacement level, while MRHA reached its peak at a 0.5% replacement level. However, a comparison of the compressive strength of NRHA at 1% (22 N/mm²) against MRHA at 0.5% (21.5 N/mm²) revealed that the marginal difference in strength made MRHA a more cost-effective option due to the lower expenses involved in its preparation. Thus, MRHA presents a more economical solution for achieving comparable compressive strength. Furthermore, the study applied linear, non-linear, and mixed regression analyses to model the properties of NRHA and MRHA concrete based on a comprehensive set of variables. The analysis found that the blended ordinary and logarithmic models provided the best fit, offering superior accuracy compared to linear and non-linear models.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-024-00043-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142798332","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 improved prediction of high-performance concrete compressive strength using ensemble models and neural networks","authors":"Umar Jibrin Muhammad,&nbsp;Ismail I. Aminu,&nbsp;Ismail A. Mahmoud,&nbsp;U. U. Aliyu,&nbsp;A. G. Usman,&nbsp;Mahmud M. Jibril,&nbsp;Salim Idris Malami,&nbsp;Sani I. Abba","doi":"10.1007/s43503-024-00040-8","DOIUrl":"10.1007/s43503-024-00040-8","url":null,"abstract":"<div><p>Traditional methods for proportioning of high-performance concrete (HPC) have certain shortcomings, such as high costs, usage constraints, and nonlinear relationships. Implementing a strategy to optimize the mixtures of HPC can minimize design expenses, time spent, and material wastage in the construction sector. Due to HPC's exceptional qualities, such as high strength (HS), fluidity and resilience, it has been broadly used in construction projects. In this study, we employed Generalized Regression Neural Network (GRNN), Nonlinear AutoRegressive with exogenous inputs (NARX neural network), and Random Forest (RF) models to estimate the Compressive Strength (CS) of HPC in the first scenario. In contrast, the second scenario involved the development of an ensemble model using the Radial Basis Function Neural Network (RBFNN) to detect inferior performance of standalone model combinations. The output variable was the 28 Days CS in MPa, while the input variables included slump (S), water-binder ratio (W/B) %, water content (W) kg/m³, fine aggregate ratio (S/a) %, silica fume (SF)%, and superplasticizer (SP) kg/m³. An RF model was developed by using R Studio; GRNN and NARX-NN models were developed by using the MATLAB 2019a toolkit; and the pre- and post-processing of data was carried out by using E-Views 12.0. The results indicate that in the first scenario, the Combination M1 of the RF model outperformed other models, with greater prediction accuracy, yielding a PCC of 0.854 and MAPE of 4.349 during the calibration phase. In the second scenario, the ensemble of RF models surpassed all other models, achieving a PCC of 0.961 and MAPE of 0.952 during the calibration phase. Overall, the proposed models demonstrate significant value in predicting the CS of HPC.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-024-00040-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142757897","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 crippling load of I-shaped steel columns by using soft computing techniques","authors":"Rashid Mustafa","doi":"10.1007/s43503-024-00038-2","DOIUrl":"10.1007/s43503-024-00038-2","url":null,"abstract":"<div><p>This study is primarily aimed at creating three machine learning models: artificial neural network (ANN), random forest (RF), and k-nearest neighbour (KNN), so as to predict the crippling load (CL) of I-shaped steel columns. Five input parameters, namely length of column (<i>L</i>), width of flange (<i>b</i>_f), flange thickness (<i>t</i>_f), web thickness (<i>t</i>_w) and height of column (<i>H</i>), are used to compute the crippling load (CL). A range of performance indicators, including the coefficient of determination (<i>R</i>²), variance account factor (VAF), a-10 index, root mean square error (RMSE), mean absolute error (MAE) and mean absolute deviation (MAD), are used to assess the effectiveness of the established machine learning models. The results show that all of the three ML (machine learning) models can accurately predict the crippling load, but the performance of ANN is superior: it delivers the highest value of <i>R</i>² = 0.998 and the lowest value of RMSE = 0.008 in the training phase, as well as the highest value of <i>R</i>² = 0.996 and the smaller value of RMSE = 0.012 in the testing phase. Additional methods, including rank analysis, reliability analysis, regression plot, Taylor diagram and error matrix plot, are employed to assess the models’ performance. The reliability index (<i>β</i>) of the models is calculated by using the first-order second moment (FOSM) technique, and the result is compared with the actual value. Additionally, sensitivity analysis is performed to check the impact of the input variables on the output (CL), finding that <i>b</i>_f has the greatest impact on the crippling load, followed by <i>t</i>_f, <i>t</i>_w, <i>H</i> and <i>L</i>, in that order. This study demonstrates that ML techniques are useful for developing a reliable numerical tool for measuring the crippling load of I-shaped steel columns. It is found that the proposed techniques can also be used to predict other kinds of failures as well as different kinds of perforated columns.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-024-00038-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636653","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":"The effect of geotechnical soil properties on cbr value: review","authors":"Botlhe B. Pule,&nbsp;Jerome A. Yendaw","doi":"10.1007/s43503-024-00039-1","DOIUrl":"10.1007/s43503-024-00039-1","url":null,"abstract":"<div><p>This review paper summarizes the current state of research on relationships between geotechnical soil’s properties and the California Bearing Ratio (CBR) value. Geotechnical elements are pivotal in preventing civil engineering projects from collapses and settlement failures, so understanding detailed soil properties is an important task. CBR tests are used to assess the stiffness modulus and shear strength and guide the overlaying layer’s thickness in pavement designs. Despite such tests’ high expense and complexity, researchers have explored correlations and machine learning for CBR prediction from soil properties. This paper would delve into the varying influence of such properties as compaction properties (OMC and MDD) and index properties (LL, PL, and PI). By measuring the relevance of these properties to CBR, this paper examines their significance and potential interactions. In sum, this review sheds light on soil properties’ multifaceted effects on CBR value and provides support for informed pavement engineering decisions.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-024-00039-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595580","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":"Localization of underground pipeline intrusion sources using cross-correlation CNN: application in pile-driving model test","authors":"Fu Chai,&nbsp;Biao Zhou,&nbsp;Xiongyao Xie,&nbsp;Zixin Zhang,&nbsp;Jianyong Han","doi":"10.1007/s43503-024-00037-3","DOIUrl":"10.1007/s43503-024-00037-3","url":null,"abstract":"<div><p>Preserving the structural integrity of critical infrastructure systems necessitates a heightened focus on fortifying the protection of underground pipelines. To this end, this paper presents an innovative approach, namely the Multi-Sample Joint Localization Method (MSJLM) utilizing Cross-Correlation Convolutional Neural Networks (CC-CNN), aimed at precisely localizing intrusion sources in the vicinity of underground pipelines. Traditional techniques for detecting and pinpointing pipeline intrusions primarily rely on a single sensor monitoring point, which is susceptible to inherent errors and constraints. In contrast, the MSJLM proposed in this study leverages data from multiple samples, integrating diverse data sources through correlation analyses to elevate precision and reliability. The utilization of the CC-CNN framework for processing aggregated data has proven highly successful in extracting spatial features and identifying patterns. Furthermore, the effectiveness of this method is corroborated through validation via a pile-driving model test.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-024-00037-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142452998","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":"Usages of metaheuristic algorithms in investigating civil infrastructure optimization models; a review","authors":"Saeedeh Ghaemifard,&nbsp;Amin Ghannadiasl","doi":"10.1007/s43503-024-00036-4","DOIUrl":"10.1007/s43503-024-00036-4","url":null,"abstract":"<div><p>Optimization is the process of creating the best possible outcome while taking into consideration the given conditions. The ultimate goal of optimization is to maximize or minimize the desired effects to meet the technological and management requirements. When faced with a problem that has several possible solutions, an optimization technique is used to identify the best one. This involves checking different search domains at the right time, depending on the specific problem. To solve these optimization problems, nature-inspired algorithms are used as part of stochastic methods. In civil engineering, numerous design optimization problems are nonlinear and can be difficult to solve via traditional techniques. In such points, metaheuristic algorithms can be a more useful and practical option for civil engineering usages. These algorithms combine randomness and decisive paths to compare multiple solutions and select the most satisfactory one. This article briefly presents and discusses the application and efficiency of various metaheuristic algorithms in civil engineering topics.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-024-00036-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142411192","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":"Optimizing the bituminous pavement constructions with waste plastic materials improved the road constructions performance and their future applications","authors":"M. Lalitha Pallavi,&nbsp;Subhashish Dey,&nbsp;Ganugula Taraka Naga Veerendra,&nbsp;Siva Shanmukha Anjaneya Babu Padavala,&nbsp;Akula Venkata Phani Manoj","doi":"10.1007/s43503-024-00035-5","DOIUrl":"10.1007/s43503-024-00035-5","url":null,"abstract":"<div><p>The yearly production of plastic garbage is rising in the current environment as a result of the fast population rise. Recycling and reusing plastic trash is essential for sustainable development. The need of the hour is to utilize waste polythene for various supporting reasons since it is not biodegradable. These materials are made of polymers like polyethylene, polypropylene, and polystyrene. Due to the enhanced performance and elimination of the environmental issue, adding plastic waste to flexible pavement has emerged as a desirable choice. A composite material known as bituminous concrete (BC) is often utilized in construction projects such as road paving, airport terminals, and stopover areas. It includes mineral aggregate and black top or bitumen, which are combined, laid down in layers, and then compacted. The bituminous mixture in this research article was combined with plastic to use a chemical stabilizer. The ideal bitumen content is replaced by 0, 15%, 27%, and 36% plastic, as well as the bitumen's weight, stability, and Marshall value to create hypothermal. A linear scale is used to compare the flow rates to the bituminous mixture. The characterization of plastics contains bituminous materials are done by the SEM–EDX, XRD, FTIR and BET analysis. There have been several studies on the addition of trash to bituminous mixes, but this one is focused on the use of plastic waste as a modification in a bitumen binder for flexible pavement. According to research, bituminous mixes containing up to 4 percent plastic waste are excellent for sustainable development.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-024-00035-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142415241","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":"Evaluating fine tuned deep learning models for real-time earthquake damage assessment with drone-based images","authors":"Furkan Kizilay,&nbsp;Mina R. Narman,&nbsp;Hwapyeong Song,&nbsp;Husnu S. Narman,&nbsp;Cumhur Cosgun,&nbsp;Ammar Alzarrad","doi":"10.1007/s43503-024-00034-6","DOIUrl":"10.1007/s43503-024-00034-6","url":null,"abstract":"<div><p>Earthquakes pose a significant threat to life and property worldwide. Rapid and accurate assessment of earthquake damage is crucial for effective disaster response efforts. This study investigates the feasibility of employing deep learning models for damage detection using drone imagery. We explore the adaptation of models like VGG16 for object detection through transfer learning and compare their performance to established object detection architectures like YOLOv8 (You Only Look Once) and Detectron2. Our evaluation, based on various metrics including mAP, mAP50, and recall, demonstrates the superior performance of YOLOv8 in detecting damaged buildings within drone imagery, particularly for cases with moderate bounding box overlap. This finding suggests its potential suitability for real-world applications due to the balance between accuracy and efficiency. Furthermore, to enhance real-world feasibility, we explore two strategies for enabling the simultaneous operation of multiple deep learning models for video processing: frame splitting and threading. In addition, we optimize model size and computational complexity to facilitate real-time processing on resource-constrained platforms, such as drones. This work contributes to the field of earthquake damage detection by (1) demonstrating the effectiveness of deep learning models, including adapted architectures, for damage detection from drone imagery, (2) highlighting the importance of evaluation metrics like mAP50 for tasks with moderate bounding box overlap requirements, and (3) proposing methods for ensemble model processing and model optimization to enhance real-world feasibility. The potential for real-time damage assessment using drone-based deep learning models offers significant advantages for disaster response by enabling rapid information gathering to support resource allocation, rescue efforts, and recovery operations in the aftermath of earthquakes.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-024-00034-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142414177","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":"Predicting the compressive strength of self-compacting concrete using artificial intelligence techniques: a review","authors":"Sesugh Terlumun,&nbsp;M. E. Onyia,&nbsp;F. O. Okafor","doi":"10.1007/s43503-024-00029-3","DOIUrl":"10.1007/s43503-024-00029-3","url":null,"abstract":"<div><p>Concrete is one of the most common construction materials used all over the world. Estimating the strength properties of concrete traditionally demands extensive laboratory experimentation. However, researchers have increasingly turned to predictive models to streamline this process. This review focuses on predicting the compressive strength of self-compacting concrete using artificial intelligence (AI) techniques. Self-compacting concrete represents an advanced construction material particularly suited for scenarios where traditional vibrational methods face limitations due to intricate formwork or reinforcement complexities. This review evaluates various AI techniques through a comparative performance analysis. The findings highlight that employing Deep Neural Network models with multiple hidden layers significantly enhances predictive accuracy. Specifically, artificial neural network (ANN) models exhibit robustness, consistently achieving R² values exceeding 0.7 across reviewed studies, thereby demonstrating their efficacy in predicting concrete compressive strength. The integration of ANN models is recommended for formulating various civil engineering properties requiring predictive capabilities. Notably, the adoption of AI models reduces both time and resource expenditures by obviating the need for extensive experimental testing, which can otherwise delay construction activities.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-024-00029-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142414684","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}