AI in civil engineering最新文献

{"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}

{"title":"Application of machine learning in predicting mechanical properties of sandcrete blocks made from quarry dust: a review","authors":"John Igeimokhia Braimah,&nbsp;Wasiu Olabamiji Ajagbe,&nbsp;Kolawole Adisa Olonade","doi":"10.1007/s43503-024-00033-7","DOIUrl":"10.1007/s43503-024-00033-7","url":null,"abstract":"<div><p>Quarry dust, conventionally considered waste, has emerged as a potential solution for sustainable construction materials. This paper comprehensively review the mechanical properties of blocks manufactured from quarry dust, with a particular focus on the transformative role of machine learning (ML) in predicting and optimizing these properties. By systematically reviewing existing literature and case studies, this paper evaluates the efficacy of ML methodologies, addressing challenges related to data quality, feature selection, and model optimization. It underscores how ML can enhance accuracy in predicting mechanical properties, providing a valuable tool for engineers and researchers to optimize the design and composition of blocks made from quarry dust. This synthesis of mechanical properties and ML applications contributes to advancing sustainable construction practices, offering insights into the future integration of technology for predictive modeling in material science.</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/s43503-024-00033-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142414531","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":"Enhancing structural stability in civil structures using the bi-directional evolutionary structural optimization method","authors":"Tao Xu,&nbsp;Xiaodong Huang,&nbsp;Xiaoshan Lin,&nbsp;Yi Min Xie","doi":"10.1007/s43503-024-00031-9","DOIUrl":"10.1007/s43503-024-00031-9","url":null,"abstract":"<div><p>Topology optimization techniques are increasingly utilized in structural design to create efficient and aesthetically pleasing structures while minimizing material usage. Many existing topology optimization methods may generate slender structural members under compression, leading to significant buckling issues. Consequently, incorporating buckling considerations is essential to ensure structural stability. This study investigates the capabilities of the bi-directional evolutionary structural optimization method, particularly its extension to handle multiple load cases in buckling optimization problems. The numerical examples presented focus on three classical cases relevant to civil engineering: maximizing the buckling load factor of a compressed column, performing buckling-constrained optimization of a frame structure, and enhancing the buckling resistance of a high-rise building. The findings demonstrate that the algorithm can significantly improve structural stability with only a marginal increase in compliance. The detailed mathematical modeling, sensitivity analyses, and optimization procedures discussed provide valuable insights and tools for engineers to design structures with enhanced stability and efficiency.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-024-00031-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142415362","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 stacking machine learning model for predicting pullout capacity of small ground anchors","authors":"Lin Li,&nbsp;Linlong Zuo,&nbsp;Guangfeng Wei,&nbsp;Shouming Jiang,&nbsp;Jian Yu","doi":"10.1007/s43503-024-00032-8","DOIUrl":"10.1007/s43503-024-00032-8","url":null,"abstract":"<div><p>Small ground anchors are widely used to fix securing tents in disaster relief efforts. Given the urgent nature of rescue operations, it is crucial to obtain prompt and accurate estimations of their pullout capacity. In this study, a stacking machine learning (ML) model is developed for the rapid estimation of pullout capacity offered by small ground anchors used for temporary tents, leveraging cone penetration data. The proposed stacking model incorporates three ML algorithms as the base regression models: K-nearest neighbors (KNN), support vector regression (SVR), and extreme gradient boosting (XGBoost). A dataset comprising 119 in-situ anchor pullout tests, where the cone penetration data were measured, is utilized to train and assess the stacking model performance. Three metrics, i.e., coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE), are employed to evaluate the predictive accuracy of the proposed model and compare its performance against four popular ML models and an empirical formula to highlight the advantages of the proposed stacking approach. The results affirm that the proposed stacking model outperforms other ML models and the empirical approach as achieving higher R² and lower MAE and RMSE and more predicted data points falling within 20% error line. Thus, the proposed stacking model holds promising potential as a solution for efficiently predicting the pullout capacity of small ground anchors.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-024-00032-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142415051","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":"Efficient machine learning model for settlement prediction of large diameter helical pile in c—Φ soil","authors":"Nur Mohammad Shuman,&nbsp;Mohammad Sadik Khan,&nbsp;Farshad Amini","doi":"10.1007/s43503-024-00028-4","DOIUrl":"10.1007/s43503-024-00028-4","url":null,"abstract":"<div><p>Machine learning is frequently used in various geotechnical applications nowadays. This study presents a statistics and machine learning model for settlement prediction of helical piles that relates compressive service load and soil parameters as a group with the pile parameters. Machine learning algorithms such as Decision Trees, Random Forests, AdaBoost, and Artificial Neural Networks (ANN) were used to develop the predictive models. The models were validated using cross-validation techniques and tested on an independent dataset to assess their accuracy and generalizability. Numerical investigation is used here to supplement the field data by simulating various soil conditions and pile geometries that have not been tested in the field. This study compiled numerical results of 3600 models. As the models are well-calibrated and validated, the data from these models can be reasonably assumed to simulate the ground situation. At the end of this study, a comparative analysis of statistic learning and machine learning (ML) was done using the field axial load tests database and numerical investigation on helical piles. It is observed that ML models like Decision Trees and Random Forests provided the better model with R-squared values of 0.92 and 0.96, respectively, for large diameters. The authors believe this study will permit engineers and state agencies to understand this prediction model's efficacy better, resulting in a more resilient approach to designing large-diameter helical piles for the compressive load.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-024-00028-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142414651","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":"Application of bi-directional evolutionary structural optimization to the design of an innovative pedestrian bridge","authors":"Yaping Lai,&nbsp;Yu Li,&nbsp;Yanchen Liu,&nbsp;Peixin Chen,&nbsp;Lijun Zhao,&nbsp;Jin Li,&nbsp;Yi Min Xie","doi":"10.1007/s43503-024-00027-5","DOIUrl":"10.1007/s43503-024-00027-5","url":null,"abstract":"<div><p>With rapid advances in design methods and structural analysis techniques, computational generative design strategies have been adopted more widely in the field of architecture and engineering. As a performance-based design technique to find out the most efficient structural form, topology optimization provides a powerful tool for designers to explore lightweight and elegant structures. Building on this background, this study proposes an innovative pedestrian bridge design, which covers the process from conceptualization to detailed design implementation. This pedestrian bridge, with a main span of 152 m, needs to meet some unique architectural requirements, while addressing multiple engineering challenges. Aiming to reduce the depth of the girder but still meeting the load-carrying capacity requirements, the superstructure of this bridge adopts a variable-depth spinal-shaped girder in the center of its deck, thus forming an elegant curving facade, from which one pathway cantilevers on either side. At one end of the bridge, given considerable elevation difference between the bridge deck and the ground, a two-level Fibonacci-type spiral-shaped bicycle ramp is provided. The superstructure is supported by a series of organic tree-shaped branching piers resulting from the topology optimization. The ingenious design for the elegant profile of the bicycle ramp generates an enjoyable and dynamic crossing experience, with scenic views in all directions. By virtue of technological innovation, the pedestrian bridge is expected to create an iconic, cost-effective, and low-maintenance solution. A brief overview of the theoretical background of the bi-directional evolutionary structure optimization (BESO) and the multi-material BESO approach is also offered in this paper, while the construction requirements and challenges, conceptual development process, form-finding strategy, detailed design, and construction method of the bridge are presented.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-024-00027-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141357308","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":"Mechanical characteristics of auxetic composite honeycomb sandwich structure under bending","authors":"Hang Hang Xu,&nbsp;Xue Gang Zhang,&nbsp;Dong Han,&nbsp;Wei Jiang,&nbsp;Yi Zhang,&nbsp;Yu Ming Luo,&nbsp;Xi Hai Ni,&nbsp;Xing Chi Teng,&nbsp;Yi Min Xie,&nbsp;Xin Ren","doi":"10.1007/s43503-024-00026-6","DOIUrl":"10.1007/s43503-024-00026-6","url":null,"abstract":"<div><p>Auxetic honeycomb sandwich structures (AHS) composed of a single material generally exhibit comparatively lower energy absorption (EA) and platform stress, as compared to traditional non-auxetic sandwich structures (TNS). To address this limitation, the present study examines the use of aluminum foam (AF) as a filling material in the re-entrant honeycomb sandwich structure (RS). Filling the AHS with AF greatly enhances both the EA and platform stress in comparison to filling the TNS with AF, while the auxetic composite honeycomb sandwich structure effectively addresses interface delamination observed in traditional non-auxetic composite sandwich structures. Subsequently, the positive–negative Poisson’s ratio coupling designs are proposed to strengthen the mechanical features of a single honeycomb sandwich structure. The analysis results show that the coupling structure optimizes the mechanical properties by leveraging the high bearing capacity of the hexagonal honeycomb and the great interaction between the re-entrant honeycomb and the filling material. In contrast with traditional non-auxetic sandwich structures, the proposed auxetic composite honeycomb sandwich structures demonstrate superior EA and platform stress performance, suggesting their immense potential for utilization in protective engineering.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-024-00026-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140981693","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":"Study on the use of different machine learning techniques for prediction of concrete properties from their mixture proportions with their deterministic and robust optimisation","authors":"Sumanta Mandal,&nbsp;Amit Shiuly,&nbsp;Debasis Sau,&nbsp;Achintya Kumar Mondal,&nbsp;Kaustav Sarkar","doi":"10.1007/s43503-024-00024-8","DOIUrl":"10.1007/s43503-024-00024-8","url":null,"abstract":"<div><p>The construction industry relies so heavily on concrete that it's crucial to precisely forecast and optimize the strength and workability of concrete mixtures, while reducing costs as much as possible. For this objective, this study tries to predict and optimize the compressive strength and workability (slump) of concrete by using deterministic and robust optimization approaches, so as to determine the optimum concrete mixture proportions, while minimizing cost. Specifically, strength and slump were predicted based on concrete mixture proportions with five different machine learning techniques—support vector machine (SVM), artificial neural network (ANN), fuzzy inference system (FIS), adaptive fuzzy inference system (ANIS), and genetic expression programming (GEP), based on a dataset comprising two hundred concrete mixtures, which has various levels of key ingredients, including cement, water, fine aggregate, coarse aggregate, and size of coarse aggregate, along with their associated measures of strength and workability. These ingredients were used as input parameters, while compressive strength and slump (representing workability) served as output parameters for each mix proportion. Experimental investigations were conducted on fifteen distinct concrete mixes to validate the performance of the five networks, finding that ANFIS can yield the best results both for training and validation. This study provides valuable insights for predicting concrete properties and optimizing concrete mixture proportions, thus helping to maximize strength and workability while minimizing costs.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-024-00024-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140727690","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":"aiWATERS: an artificial intelligence framework for the water sector","authors":"Darshan Vekaria,&nbsp;Sunil Sinha","doi":"10.1007/s43503-024-00025-7","DOIUrl":"10.1007/s43503-024-00025-7","url":null,"abstract":"<div><p>The presence of Artificial Intelligence (AI) and Machine Learning (ML) applications has led to its widespread adoption across diverse domains. AI is making its way into industry, beyond research and academia. Concurrently, the water sector is undergoing a digital transformation. Water utilities in the United States are at different stages in their journey of digital transformation, and the decision makers in water sector, who are non-expert stakeholders in AI applications, need to better understand this technology to make informed decisions. While AI has numerous benefits to offer, there are also many challenges related to data, model development, knowledge integration and ethical concerns that should be considered before implementing it for real world applications. Civil engineering is a licensed profession where critical decision making is involved. Therefore, trust in any decision support technology is critical for its acceptance in real-world applications. Therefore, this research proposes a framework called <i>ai</i>WATERS (Artificial Intelligence for the Water Sector) which can serve as a guide for the water utilities to successfully implement AI in their system. Based on this framework, we conduct pilot interviews and surveys with various small, medium, and large water utilities in the United States (US) to capture their current state of AI implementation and identify the challenges faced by them. The research findings reveal that most of the water utilities in the United States are at an early stage of implementing AI as they face concerns regarding the black box nature, trustworthiness, and sustainability of AI technology in their system. The <i>ai</i>WATERS framework is intended to help the utilities navigate through these issues in their journey of digital transformation.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-024-00025-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140732536","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":"Improving the efficiency of isolated-footing resting on loose sand soil using grout diaphragm walls: an experimental and numerical study","authors":"Beshoy Maher Hakeem","doi":"10.1007/s43503-024-00023-9","DOIUrl":"10.1007/s43503-024-00023-9","url":null,"abstract":"<div><p>In light of rising loads from several sources, including additional stories, eccentric loads, and increased live loads, foundations often face increased demands. To address this, horizontal reinforcements are now commonly positioned beneath footings to enhance the bearing capacity of the loose-dense sand subgrade. By grouting on both sides of the footing, not only can vertical settlement be minimized, but also the soil movement in the horizontal direction under the chosen loaded footing can be reduced. The objective of this study is to conduct extensive experimental work on twenty-one (21) soil models to assess the efficiency of a circular footing resting on granular soil injected with grout diaphragm walls. Specifically, this study investigated the bearing capacity of granular soil in relation to the breadth (b) and length (L) of grouted walls. The results showed that, installing grouted wall injection on both sides of the existing footing is an excellent method to improve the bearing capacity of the subgrade layer. To check the validity of the chosen computational processes, both PLAXIS (3D) software and a 2D Finite Element Program GeoStudio 2018 were used. The findings indicate a direct correlation with the experimental observations in that the reinforcement has a considerable effect on the bearing capacity of a circular-footing resting on granular soil.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-024-00023-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140749681","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}