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Mohammad Al-Nawaiseh
Department of Civil Engineering, Faculty of Engineering, Amman Arab University, Amman,
Civil Engineering, Building, Construction & Architecture

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Predicting the Inelastic Response of Base Isolated Structures Utilizing Regression Analysis and Artificial Neural Network Mohammad Al-Rawashdeh; Isam Yousef; Mohammad Al-Nawaiseh
Civil Engineering Journal Vol 8, No 6 (2022): June
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2022-08-06-07

Abstract

Indeed, utilizing a base isolation system in RC structures can remarkably minimize the possibility of failure, particularly in seismic-prone countries. Despite that, the design of these structures is a long procedure that consists of choosing the appropriate isolator to optimize the nonlinear behavior of the superstructure. Moreover, the numerical simulations require huge computational effort when high accuracy is required. In recent decades, scientists and engineers have applied numerous estimation approaches such as multiple linear regression and artificial neural networks to decrease the required cost and time for daily design problems. Thus, this study's main objective is to solve the difficulty of rapid response prediction by using soft-computing techniques. Additionally, it aims to study the capability of multiple linear regression and artificial neural networks in estimating the seismic performance of base-isolated RC structures under earthquakes. A nonlinear response history analysis of four different lead rubber-bearing isolated RC structures will be performed in order to determine the responses of these structures. Subsequently, the prediction models will be developed using the responses of the structures as inputs for multiple linear regression and artificial neural networks. Lastly, the reliability of both estimation approaches in terms of the response of base-isolated structures will be investigated by comparing the prediction models' capability. In general, the results of the study show that artificial neural networks provide considerably better accuracy in estimating base-isolated structures compared to multiple linear regression, and their performance results in reliable prediction. Doi: 10.28991/CEJ-2022-08-06-07 Full Text: PDF
Performance of Mortar Incorporating Heat-Treated Drinking Water Treatment Sludge as a Silica-Sand Replacement Mohammad Al-Rawashdeh; Ahmed Alzoubi; Shadi Hanandeh; Isam Yousef; Mohammad Al-Nawaiseh
Civil Engineering Journal Vol 8, No 8 (2022): August
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2022-08-08-08

Abstract

This paper examines the possibility of using water purification wastes in the production of mortar. Within the study context, XRD and XRF analyses were performed to obtain the chemical composition of sludge. Moreover, heat-treated sludge at a temperature of 900ºC was used in the preparation of mortar mixes as a partial sand replacement (5, 10, 15, and 20% by sand weight) with a w/c of 0.48. Fresh mortars were tested for workability, and mortar samples with 7, 28, and 90 days curing ages were tested for dry density, absorption, ultrasonic pulse velocity (UPV), and compressive and flexural strengths. Besides, some regression modeling was conducted for each of the measured parameters. In general, the results showed that the use of up to 10% incinerated sludge by sand weight leads to a slight decrease in the workability and density of the mixture and a 10% increase in its strength. Nevertheless, mortars with sludge content of over 10% showed a significant increase in water absorption and a decrease in strength and other properties. Doi: 10.28991/CEJ-2022-08-08-08 Full Text: PDF
Co-Authors Ahmed Alzoubi Isam Yousef Mohammad Al-Rawashdeh Shadi Hanandeh