The Prediction of Self-Healing Capacity of Bacteria-Based Concrete Using Machine Learning Approaches

Zhuang, Xiaoying; Zhou, Shuai

doi:10.32604/cmc.2019.04589

Cited by 45 publications

(25 citation statements)

References 55 publications

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“…These proposed empirical equations predict the strength and strain capacity of confined concrete members with low accuracy [46]. The use of artificial neural networks (ANNs) for modeling various complex problems in structural engineering is increasing [47][48][49][50][51][52]. One can determine and capture the interactions between various variables of a complex system by using ANNs, despite the unknown and usually unpredictable nature of these interactions.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Properties of FRP-Confined Concrete Cylinders Based on Artificial Neural Networks

2020

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Recently, the use of fiber-reinforced polymers (FRP)-confinement has increased due to its various favorable effects on concrete structures, such as an increase in strength and ductility. Therefore, researchers have been attracted to exploring the behavior and efficiency of FRP-confinement for concrete structural elements further. The current study investigates improved strength and strain models for FRP confined concrete cylindrical elements. Two new physical methods are proposed for use on a large preliminary evaluated database of 708 specimens for strength and 572 specimens for strain from previous experiments. The first approach is employing artificial neural networks (ANNs), and the second is using the general regression analysis technique for both axial strength and strain of FRP-confined concrete. The accuracy of the newly proposed strain models is quite satisfactory in comparison with previous experimental results. Moreover, the predictions of the proposed ANN models are better than the predictions of previously proposed models based on various statistical indices, such as the correlation coefficient (R) and mean square error (MSE), and can be used to assess the members at the ultimate limit state.

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Section: Introductionmentioning

confidence: 99%

Prediction of Properties of FRP-Confined Concrete Cylinders Based on Artificial Neural Networks

2020

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“…In supervised learning, which is also called learning with teacher or guided learning, labeled data with a desired output are provided as input to the machine. However, in the case of unsupervised learning, which is also referred to as learning without a teacher, no labeled input data are provided to the machine [ 31 , 32 , 33 , 34 , 35 , 36 ]. The machine instead tries to draw inferences from the dataset containing unlabeled responses.…”

Section: Artificial Intelligence and Its Application In Shear Strementioning

confidence: 99%

A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering

Sharma

Ahmed

Naseem

et al. 2021

Sensors

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Ensuring soil strength, as well as preliminary construction cost and duration prediction, is a very crucial and preliminary aspect of any construction project. Similarly, building strong structures is very important in geotechnical engineering to ensure the bearing capability of structures against external forces. Hence, in this first-of-its-kind state-of-the-art review, the capability of various artificial intelligence (AI)-based models toward accurate prediction and estimation of preliminary construction cost, duration, and shear strength is explored. Initially, background regarding the revolutionary AI technology along with its different models suited for geotechnical and construction engineering is presented. Various existing works in the literature on the usage of AI-based models for the abovementioned applications of construction and maintenance are presented along with their advantages, limitations, and future work. Through analysis, various crucial input parameters with great impact on the estimation of preliminary construction cost, duration, and soil shear strength are enumerated and presented. Lastly, various challenges in using AI-based models for accurate predictions in these applications, as well as factors contributing to the cost-overrun issues, are presented. This study can, thus, greatly assist civil engineers in efficiently using the capabilities of AI for solving complex and risk-sensitive tasks, and it can also be used in Internet of things (IoT) environments for automated applications such as smart structural health-monitoring systems.

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“…This subsection shows of our framework's evaluation. We applied KDDTest+ and KDDTest-21 datasets and following different machine learning classifiers: Naive Bayes (NB) [34,35], Logistic Regression (LR) [36,37], Jrip (JR) [38], J48 Decision Tree (J48) [39], LMT Decision Tree (LMT), Random Forest (RF), Support Vector Machine (SMO) [40][41][42], K-Nearest Neighbors (IBK) [43,44]. All classifier machine learning methods are notified in Table 5.…”

Section: Proposed Framework Evaluationmentioning

confidence: 99%

Machine learning based distributed big data analysis framework for next generation web in IoT

Singh

Cha

Kim

et al. 2021

ComSIS

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For the advancement of the Internet of Things (IoT) and Next Generation Web, various applications have emerged to process structured or unstructured data. Latency, accuracy, load balancing, centralization, and others are issues on the cloud layer of transferring the IoT data. Machine learning is an emerging technology for big data analytics in IoT applications. Traditional data analyzing and processing techniques have several limitations, such as centralization and load managing in a massive amount of data. This paper introduces a Machine Learning Based Distributed Big Data Analysis Framework for Next Generation Web in IoT. We are utilizing feature extraction and data scaling at the edge layer paradigm for processing the data. Extreme Learning Machine (ELM) is adopting in the cloud layer for classification and big data analysis in IoT. The experimental evaluation demonstrates that the proposed distributed framework has a more reliable performance than the traditional framework.

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The Prediction of Self-Healing Capacity of Bacteria-Based Concrete Using Machine Learning Approaches

Cited by 45 publications

References 55 publications

Prediction of Properties of FRP-Confined Concrete Cylinders Based on Artificial Neural Networks

Prediction of Properties of FRP-Confined Concrete Cylinders Based on Artificial Neural Networks

A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering

Machine learning based distributed big data analysis framework for next generation web in IoT

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