AI in civil engineering最新文献

{"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}

{"title":"A preliminary investigation on enabling digital twin technology for operations and maintenance of urban underground infrastructure","authors":"Xi Cheng,&nbsp;Chen Wang,&nbsp;Fayun Liang,&nbsp;Haofen Wang,&nbsp;Xiong Bill Yu","doi":"10.1007/s43503-024-00021-x","DOIUrl":"10.1007/s43503-024-00021-x","url":null,"abstract":"<div><p>Underground infrastructure plays a kind of crucial role in modern production and living, especially in big cities where the ground space has been fully utilized. In the context of recent advancements in digital technology, the demand for the application of digital twin technology in underground infrastructure has become increasingly urgent as well. However, the interaction and co-integration between underground engineering entities and virtual models remain relatively limited, primarily due to the unique nature of underground engineering data and the constraints imposed by the development of information technology. This research focuses on underground engineering infrastructure and provides an overview of the application of novel information technologies. Furthermore, a comprehensive framework for digital twin implementation, which encompasses five dimensions and combines emerging technologies, has been proposed. It thereby expands the horizons of the intersection between underground engineering and digital twins. Additionally, a practical project in Wenzhou serves as a case study, where a comprehensive database covering the project’s entire life cycle has been established. The physical model is visualized, endowed with functional implications and data analysis capabilities, and integrated with the visualization platform to enable dynamic operation and maintenance management of the project.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-024-00021-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140372145","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 and design of mechanical properties of origami-inspired braces based on machine learning","authors":"Jianguo Cai,&nbsp;Huafei Xu,&nbsp;Jiacheng Chen,&nbsp;Jian Feng,&nbsp;Qian Zhang","doi":"10.1007/s43503-024-00022-w","DOIUrl":"10.1007/s43503-024-00022-w","url":null,"abstract":"<div><p>In order to rapidly and accurately evaluate the mechanical properties of a novel origami-inspired tube structure with multiple parameter inputs, this study developed a method of designing origami-inspired braces based on machine learning models. Four geometric parameters, i.e., cross-sectional side length, plate thickness, crease weakening coefficient, and plane angles, were used to establish a mapping relationship with five mechanical parameters, including elastic stiffness, yield load, yield displacement, ultimate load, and ultimate displacement, all of which were calculated from load-displacement curves. Firstly, forward prediction models were trained and compared for single and multiple mechanical outputs. The parameter ranges were extended and refined to improve the predicted results by introducing the intrinsic mechanical relationships. Secondly, certain reverse prediction models were established to obtain the optimized design parameters. Finally, the design method of this study was verified in finite element methods. The design and analysis framework proposed in this study can be used to promote the application of other novel multi-parameter structures.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-024-00022-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140222603","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 technique for dynamic analysis of confined geomaterial subjected to vibratory load","authors":"Ammu Boban,&nbsp;Preeti Pateriya,&nbsp;Yakshansh Kumar,&nbsp;Kshitij Gaur,&nbsp;Ashutosh Trivedi","doi":"10.1007/s43503-024-00020-y","DOIUrl":"10.1007/s43503-024-00020-y","url":null,"abstract":"<div><p>Computer programming-based numerical programs are firmly established in geotechnical engineering, with rapid growth of finite element modeling and machine learning techniques gaining much attention both in practice and academia. This study is intended to expedite the dissemination of advanced computer applications in terms of finite element simulation and machine learning models by investigating the dynamic response of geomaterials subjected to vibratory loads. Several trial models were built to perform the experimental investigations with a vibratory shaker, signal generator, several accelerometers, a data collection system, and other ancillary devices. The implicit integration techniques in commercialized software were adopted for numerical simulations. After data collection from numerical simulation, models were chosen, trained, and assessed to produce predictions that were then used in this study. Several technologies, including the ensemble boosted tree, squared exponential Gaussian Process Regression (GPR), Matern 5/2 GPR, exponential GPR, and decision tree architectures (fine and medium), were used to forecast the displacement of confined geomaterial. The displacement-depth ratio was found rising to 80% in the frequency range of 5 to 25 Hz, suggesting a considerable change in the behavior of the geomaterial. The Matern 5/2 GPR model showed better accuracy with an R² value of 0.99, indicating an outstanding predictive ability. The Matern 5/2 GPR and boosted tree models could help better understand the links between displacement and its distribution along the direction of load application. The outcomes of this study based on computer-aided finite element programs can be effectively implemented in machine learning to develop computer programs. In conclusion, the computational machine learning models adopted in this study offer a new insight for uncovering hidden intrinsic laws and creating new knowledge for geotechnical researchers and practitioners.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-024-00020-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142409272","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":"Impact of waste foundry sand on drainage behavior of sandy soil: an experimental and machine learning study","authors":"Ankit Kumar,&nbsp;Aditya Parihar","doi":"10.1007/s43503-023-00019-x","DOIUrl":"10.1007/s43503-023-00019-x","url":null,"abstract":"<div><p>The study of drainage behavior is essential for using waste material in geotechnical applications. In this study, sandy soil was replaced with waste foundry sand (WFS) at an incremental interval of 20% by weight. Permeability (<i>k</i>) for each mix was acquired at three relative densities (<i>R</i>_D), i.e., 65%, 75% and 85%, by using the constant head method. Then the results were further processed with machine learning (ML) models to validate the experimental data. The experimental study demonstrated that <i>k</i> would decrease with the increase in relative density and WFS content. A rise in <i>R</i>_D from 65% to 85% resulted in a substantial reduction of up to 140% in the value of <i>k</i>. Moreover, the complete replacement of sand with WFS reduced the value of <i>k</i> by 36%, 51% and 57% for R_D of 65%, 75% and 85%, respectively. The total dataset of 90 observations was divided at a ratio of 63/13/15 into training/validation/testing datasets for ML-AI modeling. Input variables include percentage of sand (BS), replacement with WFS, total head (<i>H</i>), time interval (<i>t</i>) and outflow (<i>Q</i>); and <i>k</i> is the output variable. The methods of artificial neural network (ANN), random forest (RF), decision tree (DT) and multi-linear regression (MLR) are used for <i>k</i> prediction. It is found that the random forest approach performed outstandingly in these methods, with an <i>R</i>² value of 0.9955. The performance of all the proposed methods was compared and verified with Taylor's diagram. Sensitivity analysis showed that <i>Q</i> and <i>R</i>_D were the most influential parameters for predicting <i>k</i> values.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-023-00019-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139452246","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 brief introductory review to deep generative models for civil structural health monitoring","authors":"Furkan Luleci,&nbsp;F. Necati Catbas","doi":"10.1007/s43503-023-00017-z","DOIUrl":"10.1007/s43503-023-00017-z","url":null,"abstract":"<div><p>The use of deep generative models (DGMs) such as variational autoencoders, autoregressive models, flow-based models, energy-based models, generative adversarial networks, and diffusion models has been advantageous in various disciplines due to their high data generative skills. Using DGMs has become one of the most trending research topics in Artificial Intelligence in recent years. On the other hand, the research and development endeavors in the civil structural health monitoring (SHM) area have also been very progressive owing to the increasing use of Machine Learning techniques. As such, some of the DGMs have also been used in the civil SHM field lately. This short review communication paper aims to assist researchers in the civil SHM field in understanding the fundamentals of DGMs and, consequently, to help initiate their use for current and possible future engineering applications. On this basis, this study briefly introduces the concept and mechanism of different DGMs in a comparative fashion. While preparing this short review communication, it was observed that some DGMs had not been utilized or exploited fully in the SHM area. Accordingly, some representative studies presented in the civil SHM field that use DGMs are briefly overviewed. The study also presents a short comparative discussion on DGMs, their link to the SHM, and research directions.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10069736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI art in architecture","authors":"Joern Ploennigs,&nbsp;Markus Berger","doi":"10.1007/s43503-023-00018-y","DOIUrl":"10.1007/s43503-023-00018-y","url":null,"abstract":"<div><p>Recent diffusion-based AI art platforms can create impressive images from simple text descriptions. This makes them powerful tools for concept design in any discipline that requires creativity in visual design tasks. This is also true for early stages of architectural design with multiple stages of ideation, sketching and modelling. In this paper, we investigate how applicable diffusion-based models already are to these tasks. We research the applicability of the platforms Midjourney, DALL<span>(cdot)</span>E 2 and Stable Diffusion to a series of common use cases in architectural design to determine which are already solvable or might soon be. Our novel contributions are: (i) a comparison of the capabilities of public AI art platforms; (ii) a specification of the requirements for AI art platforms in supporting common use cases in civil engineering and architecture; (iii) an analysis of 85 million Midjourney queries with Natural Language Processing (NLP) methods to extract common usage patterns. From this we derived (iv) a workflow for creating images for interior designs and (v) a workflow for creating views for exterior design that combines the strengths of the individual platforms.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42498465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Condition transfer between prestressed bridges using structural state translation for structural health monitoring","authors":"Furkan Luleci,&nbsp;F. Necati Catbas","doi":"10.1007/s43503-023-00016-0","DOIUrl":"10.1007/s43503-023-00016-0","url":null,"abstract":"<div><p>Implementing Structural Health Monitoring (SHM) systems with extensive sensing layouts on all civil structures is obviously expensive and unfeasible. Thus, estimating the state (condition) of dissimilar civil structures based on the information collected from other structures is regarded as a useful and essential way. For this purpose, Structural State Translation (SST) has been recently proposed to predict the response data of civil structures based on the information acquired from a dissimilar structure. This study uses the SST methodology to translate the state of one bridge (<i>Bridge #1)</i> to a new state based on the knowledge acquired from a structurally dissimilar bridge (<i>Bridge #2</i>). Specifically, the Domain-Generalized Cycle-Generative (DGCG) model is trained in the Domain Generalization learning approach on two distinct data domains obtained from <i>Bridge #1</i>; the bridges have two different conditions: <i>State-H</i> and <i>State-D</i>. Then, the model is used to generalize and transfer the knowledge on <i>Bridge #1</i> to <i>Bridge #2</i>. In doing so, DGCG translates the state of <i>Bridge #2</i> to the state that the model has learned after being trained. In one scenario, <i>Bridge #2’s State-H</i> is translated to <i>State-D</i>; in another scenario, <i>Bridge #2’s State-D</i> is translated to <i>State-H</i>. The translated bridge states are then compared with the real ones via modal identifiers and mean magnitude-squared coherence (MMSC), showing that the translated states are remarkably similar to the real ones. For instance, the modes of the translated and real bridge states are similar, with the maximum frequency difference of 1.12% and the minimum correlation of 0.923 in Modal Assurance Criterion values, as well as the minimum of 0.947 in Average MMSC values. In conclusion, this study demonstrates that SST is a promising methodology for research with data scarcity and population-based structural health monitoring (PBSHM). In addition, a critical discussion about the methodology adopted in this study is also offered to address some related concerns.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9976149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}