•Empirical models for concrete mechanical strength are inaccurate and cannot accommodate new input parameters.•Machine learning models are more accurate, flexible and can be retrained with updated ...databases.•Advantages and shortcomings of ML models identified model performance is compared.•Recommendations for selecting suitable model are made based on review.•Knowledge gaps and needed future research are identified.
Accurate prediction of the mechanical properties of concrete has been a concern since these properties are often required by design codes. The emergence of new concrete mixtures and applications has motivated researchers to pursue reliable models for predicting mechanical strength. Empirical and statistical models, such as linear and nonlinear regression, have been widely used. However, these models require laborious experimental work to develop, and can provide inaccurate results when the relationships between concrete properties and mixture composition and curing conditions are complex. To overcome such drawbacks, several Machine Learning (ML) models have been proposed as an alternative approach for predicting the mechanical strength of concrete. The present study examines ML models for forecasting the mechanical properties of concrete, including artificial neural networks, support vector machine, decision trees, and evolutionary algorithms. The application of each model and its performance are critically discussed and analyzed, thus identifying practical recommendations, current knowledge gaps, and needed future research.
Applications of Machine Learning (ML) algorithms in Structural Health Monitoring (SHM) have become of great interest in recent years owing to their superior ability to detect damage and deficiencies ...in civil engineering structures. With the advent of the Internet of Things, big data and the colossal and complex backlog of aging civil infrastructure assets, such applications will increase very rapidly. ML can efficiently perform several analyses of clustering, regression and classification of damage in diverse structures, including bridges, buildings, dams, tunnels, wind turbines, etc. In this systematic review, the diverse ML algorithms used in this domain have been classified into two major subfields: vibration-based SHM and image-based SHM. The efficacy of deploying ML algorithms in SHM has been discussed and detailed critical analysis of ML applications in SHM has been provided. Accordingly, practical recommendations have been made and current knowledge gaps and future research needs have been outlined.
Visual inspection has been the most widely used technique for monitoring concrete structures in service. Inspectors visually evaluate defects based on experience, skill, and engineering judgment. ...However, this process is subjective, laborious, time-consuming, and hampered by demanding access to numerous parts of complex structures. Accordingly, the present study proposes a nearly automated inspection model based on image processing and deep learning for detecting defects in typically inaccessible areas of concrete structures. Results indicate that using the Keras classifier combined with Otsu image processing can achieve superior classification accuracy of 97.63%, 96.5%, and 96.17% for training, validation, and testing data, respectively, along with low quantification error of 1.5%, 5% and 2% for the crack length, width, and angle of orientation, respectively. The type of structural damage and its severity are identified based on the allowed range of concrete crack width for different structures, including buildings and bridges based on different international standards and codes. The proposed method can deploy unmanned aerial vehicle image acquisition to offer a nearly automated inspection platform for the colossal backlog of aging concrete structures.
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•Review demystifies use of machine learning in predicting properties of concrete.•Hyperparameters of ML along with their accuracy are critically analyzed and discussed.•Main findings of NL ...predictions of compressive strength of various concrete types are presented.•Recommendations for best practice are made and needed future research is identified.
The mixture proportioning of conventional concrete is commonly established using regression analysis of experimental data. However, such traditional empirical procedures have proven less accurate for modern complex cementitious composites. The lack of robust predictive tools for estimating the mixture composition and engineering properties of novel concretes led to deploying machine learning techniques. Although these versatile computational algorithms have proven successful in diverse applications, their performance is highly dependent on the data structure and appropriate selection of hyperparameters. Therefore, this paper demystifies the use of ML in concrete technology by systematically surveying and critically reviewing ML algorithms employed to predict the compressive strength of modern concrete mixtures. The hyperparameters of various machine learning models along with the achieved accuracy are critically analyzed and discussed. The main findings regarding machine learning predictions of compressive strength for various concrete types are presented, recommendations for best practice are made, and needed future research is identified.
Applications of Machine Learning (ML) algorithms in Structural Health Monitoring (SHM) have recently become of great interest owing to their superior ability to detect damage in engineering ...structures. ML algorithms used in this domain are classified into two major subfields: vibration-based and image-based SHM. Traditional condition survey techniques based on visual inspection have been the most widely used for monitoring concrete structures in service. Inspectors visually evaluate defects based on experience and engineering judgment. However, this process is subjective, time-consuming, and hampered by difficult access to numerous parts of complex structures. Accordingly, the present study proposes a nearly automated inspection model based on image processing, signal processing, and deep learning for detecting defects and identifying damage locations in typically inaccessible areas of concrete structures. The work conducted in this thesis achieved excellent damage localization and classification performance and could offer a nearly automated inspection platform for the colossal backlog of ageing civil engineering structures.
