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All Journal Civil Engineering Journal Indonesian Journal of Electrical Engineering and Computer Science
Al-Mattarneh, Hashem
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Civil Engineering, Building, Construction & Architecture

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Journal : Civil Engineering Journal

 1 
Comparison of Nondestructive Testing Method for Strength Prediction of Asphalt Concrete Material Al-Mattarneh, Hashem; Dahim, Mohammed
Civil Engineering Journal Vol 7, No 1 (2021): January
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/cej-2021-03091645

Abstract

Concrete is one of the most common construction materials used in rigid pavement, bridges, roads, highways, and buildings. Compressive strength is one of the most important properties of concrete, which determines its quality. This study aims to present the use of a new surface dielectric method to estimate concrete compressive strength. Six concrete mixtures were produced with compressive strengths ranging from 30 to 60 MPa. Compressive strength and strength development were determined during 28 days of curing. All concrete mixes were tested using the ASTM standard. The dielectric properties, ultrasound velocity, and rebound number of all concrete mixes were also measured at each day of curing. The results obtained from the proposed dielectric method in predicting the compressive strength of concrete were compared with the rebound hammer and ultrasonic velocity that are frequently used to evaluate the compressive strength of concrete.  The dielectric method shows a higher square correlation coefficient than the other two methods. The results also indicate that combined more than one method of nondestructive techniques will lead to higher prediction and could help to reduce some errors associated with using a certain method alone. The result indicate that the finding of this study could lead to help in reducing the time of evaluating concrete during construction and could also provide tools for practicing engineer to take decision faster with more confidence level on quality of concrete. Doi: 10.28991/cej-2021-03091645 Full Text: PDF
Artificial Intelligence Using FFNN Models for Computing Soil Complex Permittivity and Diesel Pollution Content Nimer, Hamsa; Ismail, Rabah; Rawashdeh, Adnan; Al-Mattarneh, Hashem; Khodier, Mohanad; Hatamleh, Randa; Abuaddous, Musab
Civil Engineering Journal Vol 10, No 9 (2024): September
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2024-010-09-018

Abstract

Soil pollution caused by hydrocarbons, such as diesel, poses significant risks to both human health and the ecosystem. The evaluation of soil pollution and various soil engineering applications often relies on the analysis of complex permittivity, encompassing parameters such as dielectric constant and dielectric loss. Various computational models, including theoretical physics-based models, mixture theory models, statistical empirical models, and artificial neural network (ANN) models, have been explored for computing soil complex permittivity and predicting water and pollutant content. Theoretical models require detailed data that is often unavailable, and thus have limited applicability. Mixture models tend to underestimate soil characteristics due to inaccuracies in permittivity estimation of soil phases. While empirical models are widely used, their applicability is restricted to specific soil types, datasets, and locations. ANN models offer promising predictions, accommodating nonlinear phenomena and allowing for missing information and variables. In this study, capacitive electromagnetic electrode sensors were utilized to determine the complex permittivity of soil contaminated with varying levels of diesel at different moisture levels. Theoretical mixture, empirical, and Feed Forward Neural Network (FFNN) models were employed to compute the permittivity of polluted soil based on its phases and to predict the level of diesel pollution. A comparison of these modeling approaches revealed that the FFNN model exhibited the best performance. The ANN model demonstrated superior performance metrics, including a high correlation coefficient and lower mean square error. Specifically, the correlation coefficients for the FFNN model were 0.9942 for training samples, 0.9967 for validation samples, and 0.9977 for test samples. Additionally, the ANN model yielded the lowest mean square error compared to the other three models. Doi: 10.28991/CEJ-2024-010-09-018 Full Text: PDF
Co-Authors Abu-Salem, Hani Abuaddous, Musab AlKasassbeh, Mouhammd Dahim, Mohammed Dwairi, Radwan Hatamleh, Randa Ismail, Rabah Khodier, Mohanad Nimer, Hamsa Rawashdeh, Adnan