Modeling Compressive Strength of Eco-Friendly Volcanic Ash Mortar Using Artificial Neural Networking

Amin, Muhammad; Javed, Muhammad Faisal; Khan, Kaffayatullah; Shalabi, Faisal I.; Qadir, Muhammad Ghulam

doi:10.3390/sym13112009

Cited by 13 publications

(7 citation statements)

References 84 publications

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“…The results of both ANN and experimental work provided that the highest compressive strength could be achieved by adding 20% of the Tuff powder. A similar investigation on the use of volcanic ash was conducted by Amin et al [12], but on mortar specimens. ANN and adaptive neuro-fuzzy interface systems were used.…”

Section: Introductionmentioning

confidence: 82%

“…This is attributed to the complex nature of lightweight aggregates, especially the naturally occurring types existing in different parts of the globe. Lightweight aggregate properties may change depending on its origin, mineral composition, and formation mechanism [12]. This may limit the use of such aggregate for massive lightweight concrete manufacturing, especially with the absence of accurate models to predict its mechanical properties.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, the development of prediction models based on laboratory experiments is usually based on regression analysis and does not always provide accurate results [13]. These reasons increase the potential of using machine learning methods to predict the properties of lightweight concrete [12].…”

Section: Introductionmentioning

confidence: 99%

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Optimization of Tuff Stones Content in Lightweight Concrete Using Artificial Neural Networks

Yasin,

Awwad,

Malkawi

et al. 2023

Civ Eng J

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Tuff stones are volcanic sedimentary rocks formed by the consolidation of volcanic ash. They possess unique geological properties that make them attractive for a variety of construction and architectural applications. Considerable amounts and various types of Tuff stones exist in the eastern part of Jordan. However, the use of Tuff stones often requires experimental investigations that can significantly impact the accuracy of their physical and mechanical characteristics. To ensure consistent and predictable properties in their mix design, it is essential to minimize the effects of these experimental procedures. Artificial neural networks (ANNs) have emerged as a promising tool to address such challenges, leveraging their ability to analyze complex data and optimize concrete mix design. In this research, ANNs have been used to predict the optimum content of Tuff fine aggregate to produce structural lightweight concrete with a wide range (20 to 50 MPa) of compressive strength. Three different types of Tuff aggregates, namely gray, brown, and yellow Tuff, were experimentally investigated. A set of 68 mixes was produced by varying the fine-tuff aggregate content from 0 to 50%. Concrete cubes were cast and tested for their compressive strength. These samples were then used to form the input dataset and targets for ANN. ANN was created by incorporating the recent advancements in deep learning algorithms, and then it was trained, validated using data collected from the literature, and tested. Both experimental and ANN results showed that the optimum content of the various types of used Tuff fine aggregate ranges between 20 to 25%. The results revealed that there is a clear agreement between the predicted values using ANN and the experimental ones. The use of ANNs may help to cut costs, save time, and expand the applications of Tuff aggregate in lightweight concrete production. Doi: 10.28991/CEJ-2023-09-11-013 Full Text: PDF

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Section: Introductionmentioning

confidence: 82%

Section: Introductionmentioning

confidence: 99%

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Optimization of Tuff Stones Content in Lightweight Concrete Using Artificial Neural Networks

Yasin,

Awwad,

Malkawi

et al. 2023

Civ Eng J

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“…On the other hand, the varying SP content reduced the addition of high-water demand, reducing workability and strength metrics. As a result, the early age strength growth in the GGBFS-based SCC mix is more significant than in the FA-based SCC mix [25].…”

Section: Compressive Strength Test On Sccmentioning

confidence: 99%

Evaluation of the Rheological and Durability Performance of Sustainable Self-Compacting Concrete

et al. 2023

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Self-Compacting Concrete (SCC) is a special concrete that can flow easily across congested reinforcements. Also, it is easy to work with and does not segregate. The present investigation aims at the design and development of sustainable SCC with the employment of industrial by-products such as Fly Ash (FA), Ground Granulated Blast Furnace Slag (GGBFS), and Expanded Perlite Aggregate (EPA). Four SCC mixes were developed to attain a target strength of 30 MPa. Workability tests (slump flow, J-ring, and V-funnel tests) were performed following the EFNARC guidelines to ensure fresh SCC properties. Detailed experiments were conducted to evaluate the durability characteristics of the developed SCC, such as water absorption, sorptivity, acid attacks (sulphuric, nitric, sulphate, and chloride), the Rapid Chloride Penetration Test (RCPT), and finally, the elevated temperature test. Weight loss, strength loss, and physical observations of the acid and temperature effects of SCC mixes were evaluated. Also, the study focuses on the cost and sustainable index of SCC mixes and compares them with OPC mixes. From the experimental data analysis, it was observed that the developed SCC showed excellent physical and mechanical properties with a considerable reduction in cement content. SCC specimens with FA and EPA exhibit excellent acid and temperature resistance. Following the sustainable analysis, it was noted that SCC mixes reduce about 15–17.2% of carbon emissions compared to the OPC mix.

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“…Ding, HW et al proved through simulation experiments that using KPCA method to reduce the data dimension and modify the initial value and loss function of the BP neural network can improve the learning ability of the BP neural network, and the learning accuracy is improved [46]. Muhammad Nasir Amin et al used a neural network and ANFIS to predict the compressive strength of VAM by the sixfold symmetry of concrete failure [47]. Based on the algorithm model of prediction and neural network (ST-BPN), Haiming Liu et al established an improved M-CNN (Convolutional Neural Network) model to search and track underwater targets.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of Waterway Cargo Transportation Volume to Support Maritime Transportation Systems Based on GA-BP Neural Network Optimization

2022

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Water transportation is an important part of comprehensive transportation and plays a critical role in a country’s economic development. The world’s cargo transportation is dominated by waterway transportation, and maritime transportation Systems (MTS) are the main part of the waterway transportation system. The flow of goods plays a key role in the economic development of the ports along the route. The sustainable development of maritime transportation, the maritime transportation economy and the environment have great practical significance. In this paper, the principle of the BP (back propagation) neural network is used to predict the freight transportation volume of China’s waterways, and the genetic algorithm (GA) is used to optimize the BP neural network, so as to construct the GA-BPNN (back propagation neural network) prediction model. By collecting and processing the data of China’s water cargo transport volume, the experimental results show that prediction accuracy is significantly improved, which proves the reliability of the method. The experimental methods and results can provide certain reference information for the optimization, upgrade, and more scientific management of sustainable MTS in China and internationally, provide key information for port cargo handling plans, help optimize port layout, and improve transportation capacity and efficiency.

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Modeling Compressive Strength of Eco-Friendly Volcanic Ash Mortar Using Artificial Neural Networking

Cited by 13 publications

References 84 publications

Optimization of Tuff Stones Content in Lightweight Concrete Using Artificial Neural Networks

Optimization of Tuff Stones Content in Lightweight Concrete Using Artificial Neural Networks

Evaluation of the Rheological and Durability Performance of Sustainable Self-Compacting Concrete

Prediction of Waterway Cargo Transportation Volume to Support Maritime Transportation Systems Based on GA-BP Neural Network Optimization

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