NRU :: Browsing by Author "Ebid, Ahmed M."

Browsing by Author "Ebid, Ahmed M."

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Effect of desiccation on ashcrete (HSDA)-treated soft soil used as flexible pavement foundation: zero carbon stabilizer approach
(International Journal of Low-Carbon Technologies, 2022) Onyelowe, Kennedy C.; Tome, Sylvain; Ebid, Ahmed M.; Usungedo, Thompson; Van, Duc Bui; Etim, Roland K.; Onuoha, Ifeanyi C.; Attah, Imoh C.
The potential of using ashcrete to improve the microstructural, microspectral and shrinkage properties of expansive soils has been investigated under laboratory conditions. In addition to microstructural, three chemical modulus (TCM) and microspectral examinations, responses to linear shrinkage, volumetric shrinkage and crack width were also investigated using 30-day drying periods for expansive soil treated with ash cement. Moisture-related infrastructures such as the sub-floor of resilient pavements are prone to moisture by the rise and fall of the water table during seasonal changes. Therefore, the effect of soil improvement on soil morphology, chemical content and microspectral patterns was investigated. The soil was classified and characterized as (A-7-6) high plasticity soil and poor classification conditions. The hybrid sawdust ash (SDA) known as ashcrete, which has zero carbon footprint was obtained by activating SDA by mixing it with a reformulated activator material (a mixture of 8 M NaOH and a solution of NaSiO2 in a 1:1 ratio). The zero carbon cement was further used in percent-by-weight proportions of 3, 6, 9 and 12 for the soil improvement. X-ray fluorescence (XRF) and scanning electron microscopy (SEM) experiments were carried out to evaluate the pozzolanic resistance via the chemical composition of the oxide, TCM and the profile of the surface contour of the additives and the soil. XRF exposures revealed that the additives had lower pozzolanic resistance, which increased with the improved mixtures thus forming an improved soil mass. In addition, it showed that TCM silica moduli dominated soil stabilization with ashcrete. Scanning electron microscopy examination showed an increase in soil-ettringite and gel formation with the addition of ashcrete. Also, the microspectral studies of chemical oxide EDXRF and XRD have shown excellent results at 12 mass percent cement and soil cement, which has optimized aluminosilicate formation more than 70% and formation of calcite and quartz that has shown the potential of a zero carbon stabilization geomaterial ash cement as a good complementary binder.
Estimating the swelling potential of non-carbon-based binder (NCBB)-treated clayey soil for sustainable green subgrade using AI (GP, ANN and EPR) techniques
(International Journal of Low-Carbon Technologies, 2022) Onyelowe, Kennedy C.; Ebid, Ahmed M.; Onyia, Michael E; Amanamba, Ezenwa C.
A zero carbon footprint stabilization approach has been adopted in this research to improve the swelling potential (SP) of clayey soils for a greener construction approach. Construction activities like earthworks during the cement stabilization of unstable soils utilized as reconstituted subgrade materials is responsible for the emission of unhealthy amount of carbon oxides into the atmosphere contributing to ozone layer depletion and eventual global warming. This has been substituted by using eco-friendly cementing materials, quicklime activated rice husk ash (QARHA), formulated in this research work. The SP of clayey soil treated with QARHA has been predicted using the learning abilities of genetic programming (GP), artificial neural network (ANN) and the evolutionary polynomial regression (EPR). This was aimed at reducing the over dependence on repeated laboratory visits and experiments prior to infrastructure (pavement) designs, construction and future monitoring of the performance of the facility. Multiple data were collected from multiple experiments based on the tested emergent material (QARHA) treatment proportions used in this work. The data were subjected to statistical analysis and predictive model exercises. At the end, the predicted models were validated on the basis of performance and accuracy. The performance indices showed that EPR and GP with R2 of 0.997 outclassed ANN with R2 of 0.994, but EPR outclassed the two, GP and ANN with a minimal error of 6.1%. The performances of GP, ANN and EPR were compared with a previously conducted model, which utilized the learning techniques of the adaptive neuro-fuzzy interface system (ANFIS) and it was observed that EPR and GP performed better than ANFIS but ANN performed at par with it. Generally, the predictive models can predict the SP of subgrade soil treated with QARHA, a non-carbon–based binder with accuracy above 90%, which is a very good outcome.
Evolutionary Prediction of Soil Loss from Observed Rainstorm Parameters in an Erosion Watershed Using Genetic Programming
(Applied and Environmental Soil Science, 2021) Onyelowe, Kennedy C.; Ebid, Ahmed M.; Nwobia, Light
Various environmental problems such as soil degradation and landform evolutions are initiated by a natural process known as soil erosion. Aggregated soil surfaces are dispersed through the impact of raindrop and its associated parameters, which were considered in this present work as function of soil loss. In an attempt to monitor environmental degradation due to the impact of raindrop and its associated factors, this work has employed the learning abilities of genetic programming (GP) to predict soil loss deploying rainfall amount, kinetic energy, rainfall intensity, gully head advance, soil detachment, factored soil detachment, runoff, and runoff rate database collected over a three-year period as predictors. +ree evolutionary trials were executed, and three models were presented considering different permutations of the predictors. +e performance evaluation of the three models showed that trial 3 with the highest parametric permutation, i.e., that included the influence of all the studied parameters showed the least error of 0.1 and the maximum coefficient of determination (R2) of 0.97 and as such is the most efficient, robust, and applicable GP model to predict the soil loss value.
Multi-Objective Optimization of Sustainable Concrete Containing Fly Ash Based on Environmental and Mechanical Considerations
(Buildings, 2022) Onyelowe, Kennedy C.; Kontoni, Denise-Penelope N.; Ebid, Ahmed M.; Dabbaghi, Farshad; Soleymani, Atefeh; Jahangir, Hashem; Nehdi, Moncef L.
Infrastructure design, construction and development experts are making frantic efforts to overcome the overbearing effects of greenhouse gas emissions resulting from the continued dependence on the utilization of conventional cement as a construction material on our planet. The amount of CO2 emitted during cement production, transportation to construction sites, and handling during construction activities to produce concrete is alarming. The present research work is focused on proposing intelligent models for fly ash (FA)-based concrete comprising cement, fine and coarse aggregates (FAg and CAg), FA, and water as mix constituents based on environmental impact (P) considerations in an attempt to foster healthier and greener concrete production and aid the environment. FA as a construction material is discharged as a waste material from power plants in large amounts across the world. Its utilization as a supplementary cement ensures a sustainable waste management mechanism and is beneficial for the environment too; hence, this research work is a multi-objective exercise. Intelligent models are proposed for multiple concrete mixes utilizing FA as a replacement for cement to predict 28-day concrete compressive strength and life cycle assessment (LCA) for cement with FA. The data collected show that the concrete mixes with a higher amount of FA had a lesser impact on the environment, while the environmental impact was higher for those mixes with a higher amount of cement. The models which utilized the learning abilities of ANN (-BP, -GRG, and -GA), GP and EPR showed great speed and robustness with R2 performance indices (SSE) of 0.986 (5.1), 0.983 (5.8), 0.974 (7.0), 0.78 (19.1), and 0.957 (10.1) for Fc, respectively, and 0.994 (2.2), 0.999 (0.8), 0.999 (1.0), 0.999 (0.8), and 1.00 (0.4) for P, respectively. Overall, this shows that ANN-BP outclassed the rest in performance in predicting Fc, while EPR outclassed the others in predicting P. Relative importance analyses conducted on the constituent materials showed that FA had relatively good importance in the concrete mixes. However, closed-form model equations are proposed to optimize the amount of FA and cement that will provide the needed strength levels without jeopardizing the health of the environment.
Multi-Objective Prediction of the Mechanical Properties and Environmental Impact Appraisals of Self-Healing Concrete for Sustainable Structures
(Sustainability, 2022) Onyelowe, Kennedy C.; Ebid, Ahmed M.; Riofrio, Ariel; Baykara, Haci; Soleymani, Atefeh; Mahdi, Hisham A.; Jahangir, Hashem; Ibe, Kizito
As the most commonly used construction material, concrete produces extreme amounts of carbon dioxide (CO2) yearly. For this resulting environmental impact on our planet, supplementary materials are being studied daily for their potentials to replace concrete constituents responsible for the environmental damage caused by the use of concrete. Therefore, the production of bioconcrete has been studied by utilizing the environmental and structural benefit of the bacteria, Bacillus subtilis, in concrete. This bio-concrete is known as self-healing concrete (SHC) due to its potential to trigger biochemical processes which heal cracks, reduce porosity, and improve strength of concrete throughout its life span. In this research paper, the life cycle assessment (LCA) based on the environmental impact indices of global warming potential, terrestrial acidification, terrestrial eco-toxicity, freshwater eco-toxicity, marine eco-toxicity, human carcinogenic toxicity, and human noncarcinogenic toxicity of SHC produced with Bacillus subtilis has been evaluated. Secondly, predictive models for the mechanical properties of the concrete, which included compressive (Fc), splitting tensile (Ft), and flexural (Ff) strengths and slump (S), have been studied by using artificial intelligence techniques. The results of the LCA conducted on the multiple data of Bacillus subtilis-based SHC mixes show that the global warming potential of SHC-350 mix (350 kg cement mix) is 18% less pollutant than self-healing geopolymer concrete referred to in the literature study. The more impactful mix in the present study has about 6% more CO2 emissions. In the terrestrial acidification index, the present study shows a 69–75% reduction compared to the literature. The results of the predictive models show that ANN outclassed GEP and EPR in the prediction of Fc, Ft, Ff, and S with minimal error and overall performance.
Predicting Nanobinder-Improved Unsaturated Soil Consistency Limits Using Genetic Programming and Artificial Neural Networks
(Applied Computational Intelligence and Soft Computing, 2021) Ebid, Ahmed M.; Nwobia, Light I.; Onyelowe, Kennedy C.; Aneke, Frank I.
Unsaturated soils used as compacted subgrade, backfill, or foundation materials react unfavorably under hydraulically bound environments due to swell and shrink cycles in response to seasonal changes. To overcome these undesirable conditions, additive stabilization processes are used to improve the volume change phenomenon in soils. However, the use of supplementary binders made from solid waste base powder materials has become necessary to deal with the hazards of greenhouse due to ordinary cement use. Meanwhile, several studies are being carried out to design infrastructures even with the limitations of insufficient or lack of equipment needed for efficient design performance. Intelligent prediction techniques have been used to overcome this shortcoming as the primary purpose of this research work. +erefore, in this work, genetic programming (GP) and artificial neural network (ANN) have been used to predict the consistency limits, i.e., liquid limits, plastic limit, and plasticity index of unsaturated soil treated with a composite binder known as hybrid cement (HC) made from blending nanostructured quarry fines (NQF) and hydrated-lime-activated nanostructured rice husk ash (HANRHA). +e database needed for the prediction operation was generated from several experiments corresponding with treatment dosages of HANRHA between 0 and 12% at a rate of 0.1%. +e results of the stabilization exercise showed substantial development on the soil properties examined, while the prediction exercise showed that ANN outclassed GP in terms of performance evaluation, which was conducted using sum of squared error (SSE) and coefficient of determination (R2) indices. Generally, nanostructuring of the component binder material has contributed to the success achieved in both soil improvement and efficiency of the models predicted.
Predicting nanocomposite binder improved unsaturated soil UCS using genetic programming
(Nanotechnology for Environmental Engineering, 2021) Onyelowe, Kennedy C.; Ebid, Ahmed M.; Onyia, Michael E.; Nwobia, Light I.
The ability of the compacted soils and treated/compacted soils to withstand loads as foundation materials depends on the stability and durability of the soils. The design of such phenomena in treated soils whether as subgrade of pavements or embankments, backfills, etc., is a crucial phase of foundation constructions. Often, it is observed that soil mechanical and structural properties fall below the minimum design and construction requirements and this necessitates the stabilization in order to improve the needed properties. It can be observed that for this reason, there is a steady use of the laboratory and equipment prior to any design and construction as the case may be. In this work, genetic programming (GP) has been employed to predict the unconfined compressive strength of unsaturated lateritic soil treated with a hybridized binder material called hybrid cement (HC), which was formulated by blending nanotextured quarry fines (NQF) and hydrated lime activated nanotextured rice husk ash. Tests were conducted to generate multiple values for output and inputs parameters, and the values were deployed into soft computing technique to forecast UCS adopting three (3) different performance complexities (2, 3 and 4 levels of complexity). The results of the prediction models show that the four (4) levels of complexity GP model outclassed the others in performance and accuracy with a total error (SSE) of 2.4% and coefficient of determination (R2) of 0.991. Generally, GP has shown its robustness and flexibility in predicting engineering problems for use in design and performance evaluation.
Prediction and performance analysis of compression index of multiple‑binder‑treated soil by genetic programming approach
(Nanotechnology for Environmental Engineering, 2021) Onyelowe, Kennedy C.; Ebid, Ahmed M.; Nwobia, Light; Dao‑Phuc, Lam
The use and its advantage in overcoming time and equipment needs of an evolutionary prediction technique known as the genetic programming have been studied using unsaturated sample of soft soil treated with multiple binders. The soil classified as weak and highly plastic was stabilized and multiple experiments were conducted to measure the effect of the dosages of the treatment on the selected properties. The geotechnics of the exercise showed that the studied parameters substantially improved with increased proportion of hybrid cement (HC) and nanostructured quarry fines (NQF). These measured selected properties were further deployed to predict the compression index of the soil. The prediction operation proposed four-model equation by the degree of importance, sensitivity and influence of the independent parameters. This shows eventually that plasticity index has the greatest sensitivity on the compression behaviour of clay soils. The performance analysis shows that the models have very low error with model trial 4 presented in Eq. 7: CGP C = (IP−Hc⋅NQF) ( part∕ max)NQF (Ip∕wmax) Ln(wmax+3.0) , showing the least error with more consideration for the influence of more of the selected variables. It also exhibited the highest degree of determination. Generally, GP has proven to be flexible, fast and able to predict models for engineering problems for use in design and performance study.
Predictive models of volumetric stability (durability) and erodibility of lateritic soil treated with different nanotextured bio-ashes with application of loss of strength on immersion; GP, ANN and EPR performance study
(Cleaner Materials, 2021) Onyelowe, Kennedy C.; Ebid, Ahmed M.; Nwobia, Light I.
Volumetric stability and erodibility are important soil properties influenced by moisture through raindrops and eventual runoff and the rise in water tables during wet seasons. Compacted subgrade materials made of clay respond to water ingress through swelling and shrinking in turn during drying and this poses a problem for foundation structures. Supplementary cementitious materials have been used to treat soils, in a cleaner procedure to improve the mechanical properties and to overcome undesirable behavior during changes in seasons. However, design and construction of foundation structures exposed to these problems become necessary and common, which requires constant visits to the laboratory and equipment needs. In order to overcome this, machine learning‐based predictive models have been proposed in this work for the estimation of durability (Sv) via loss of strength on immersion technique and erodibility (Er) of agro‐based ashes. Genetic programming (GP) (six levels of complexity), artificial neural network (ANN) (sigmoid activation function), evolutionary polynomial regression (EPR) (GA optimized PLR method) techniques have been used to conduct this intelligent prediction exercise. The performance of the models was conducted using the sum of squared errors (SSE) and coefficient of determination (R2) indices. The results show that EPR’s Er and Sv prediction with SSE of 5.1% and 2.7% respectively and R2 of 97.2% and 92.9% respectively outclassed GP and ANN. However, both GP and ANN showed minimal error and acceptable R2 above 0.85, which showed their ability to predict with good performance accuracy.

Browsing by Author "Ebid, Ahmed M."

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