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Hisham A. Mahdi
Faculty of Engineering and Technology, Future University in Egypt, New Cairo 11865,
Civil Engineering, Building, Construction & Architecture

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Evaluating the Compressive Strength of Recycled Aggregate Concrete Using Novel Artificial Neural Network Kennedy C. Onyelowe; Tammineni Gnananandarao; Ahmed M. Ebid; Hisham A. Mahdi; M. Razzaghian Ghadikolaee; Mohammed Al-Ajamee
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-2022-08-08-011

Abstract

In this work, the compressive strength of concrete made from recycled aggregate is studied and an intelligent prediction is proposed by using a novel artificial neural network (ANN), which utilizes a sigmoid function and enables the proposal of closed-form equations. An extensive literature search was conducted, which gave rise to 476 data points containing cement, sand, aggregates, recycled aggregates of fine to coarse texture, water, and plasticizer as the constituents of the concrete and the input variables of the intelligent model. The compressive strength (fc) of the recycled aggregate concrete (RAC), which was studied through multiple experiments, was the output variable of the model. The data points of concrete strength collected through literature show a consistent and sustained strength improvement with the increase in the recycled aggregate proportions. However, the outcome of the concrete compressive strength predictive model shows remarkable performance indices as follows; r is 0.99 and 0.99, R2is 0.98 and 0.97, MSE is 28.67% and 44.64%, RMSE is 5.35% and 6.68%, MAE is 4.12% and 5.01%, and MAPE is 12.73% and 13.83% for the model training and testing respectively. These results compared well with previous studies conducted on RAC with less data, different activation functions, and different techniques. Generally, the closed-form equation, which performed at an average accuracy of 97.5% with an internal consistency of 99%, has shown its potential to be applied in RAC design and construction activities for a sustainable performance evaluation of recycled aggregate concrete. Doi: 10.28991/CEJ-2022-08-08-011 Full Text: PDF
Optimal Compressive Strength of RHA Ultra-High-Performance Lightweight Concrete (UHPLC) and Its Environmental Performance Using Life Cycle Assessment Kennedy C. Onyelowe; Ahmed M. Ebid; Hisham A. Mahdi; Ariel Riofrio; Danial Rezazadeh Eidgahee; Haci Baykara; Atefeh Soleymani; Denise-Penelope N. Kontoni; Jamshid Shakeri; Hashem Jahangir
Civil Engineering Journal Vol 8, No 11 (2022): November
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2022-08-11-03

Abstract

Frequent laboratory needs during the production of concrete for infrastructure development purposes are a factor of serious concern for sustainable development. In order to overcome this trend, an intelligent forecast of the concrete properties based on multiple data points collected from various concrete mixes produced and cured under different conditions is adopted. It is equally important to consider the impact of the concrete components in this attempt to take care of the environmental risks involved in this production. In this work, 192 mixes of an ultra-high-performance lightweight concrete (UHPLC) were collected from literature representing different mixes cured under different periods and laboratory conditions. These mix proportions constitute measured variables, which are curing age (A), cement content (C), fine aggregate (FAg), plasticizer (PL), and rice husk ash (RHA). The studied concrete property was the unconfined compressive strength (Fc). This exercise was necessary to reduce multiple dependence on laboratory examinations by proposing concrete strength equations. First, the life cycle assessment evaluation was conducted on the rice husk ash-based UHPLC, and the results from the 192 mixes show that the C-783 mix (87 kg/m3 RHA) has the highest score on the environmental performance evaluation, while C-300 (75 kg/m3 RHA) with life cycle indices of 289.85 kg CO2eq. Global warming potential (GWP), 0.66 kg SO2eq. Terrestrial acidification and 5.77 m3 water consumption was selected to be the optimal choice due to its good profile in the LCA and the Fc associated with the mix. Second, intelligent predictions were conducted by using six algorithms (ANN-BP), (ANN-GRG), (ANN-GA), (GP), (EPR), and (GMDH-Combi). The results show that (ANN-BP) with performance indices of R; 0.989, R2; 0.979, mean square error (MSE); 2252.55, root mean squared error (RMSE); 42.46 MPa and mean absolute percentage error (MAPE); 4.95% outclassed the other five techniques and is selected as the decisive model. However, it also compared well and outclassed previous models, which had used gene expression programming (GEP) and random forest regression (RFR) and achieved R2of 0.96 and 0.91, respectively. Doi: 10.28991/CEJ-2022-08-11-03 Full Text: PDF
Optimization of Green Concrete Containing Fly Ash and Rice Husk Ash Based on Hydro-Mechanical Properties and Life Cycle Assessment Considerations Kennedy C. Onyelowe; Ahmed M. Ebid; Hisham A. Mahdi; Atefeh Soleymani; Hashem Jahangir; Farshad Dabbaghi
Civil Engineering Journal Vol 8, No 12 (2022): December
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2022-08-12-018

Abstract

The development of sustainable concrete in achieving the developmental goals of the United Nations in terms of sustainable infrastructure and innovative technology forms part of the focus of this research paper. In order to move towards sustainability, the utilization of the by-products of agro-industrial operations, which are fly ash (FA) and rice husk ash (RHA), in the production of concrete has been studied. Considering the environmental impact of concrete constituents, multiple mechanical and hydraulic properties of fly ash (FA) and rice husk ash (RHA) concrete have been proposed using intelligent techniques; artificial neural network (ANN) and evolutionary polynomial regressions (EPR). Also, an intelligent mix design tool/chart for this case under study is proposed. Multiple data points of concrete materials, which were further reduced to ratios as follows; cement to binder ratio (C/B), aggregate to binder ratio (Ag/B), and plasticizer to binder ratio (PL/B) were used in this exercise. At the end of the protocol, it is observed that the constituents’ ratios are dependent on the behavior of the whole, which can be solved by using the proposed model equations and mix design charts. The models performed optimally, as none showed any performance below 80%. However, ANN, which predicted Fc03, Fc07, Fc28, Fc60, Fc90, Ft28, Ff28 & Fb28, S, Ec28 & K28, and P with an accuracy of greater than 95% each with average error of less than 9.4% each, is considered the decisive technique in predicting all the studied concrete properties, including the life cycle assessment potential of the concrete materials. Doi: 10.28991/CEJ-2022-08-12-018 Full Text: PDF
Co-Authors Ahmed M. Ebid Ariel Riofrio Atefeh Soleymani Danial Rezazadeh Eidgahee Denise-Penelope N. Kontoni Farshad Dabbaghi Haci Baykara Hashem Jahangir Jamshid Shakeri Kennedy C. Onyelowe Kennedy C. Onyelowe M. Razzaghian Ghadikolaee Mohammed Al-Ajamee Tammineni Gnananandarao