NRU :: Browsing by Author "Onyelowe, Kennedy C."

Browsing by Author "Onyelowe, Kennedy C."

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Application of 3‑algorithm ANN programming to predict the strength performance of hydrated‑lime activated rice husk ash treated soil
(Multiscale and Multidisciplinary Modeling, Experiments and Design, 2021) Onyelowe, Kennedy C.; Iqbal, Mudassir; Jalal, Fazal E.; Onyia, Michael E.; Onuoha, Ifeanyichukwu C.
Artificial neural network (ANN) method has been applied in the present work to predict the California bearing ratio (CBR), unconfined compressive strength (UCS), and resistance value (R) of expansive soil treated with recycled and activated composites of rice husk ash. Pavement foundations suffer from poor design and construction, poor material handling and utilization and management lapses. The evolutions of soft computing techniques have produced various algorithms developed to overcome certain lapses in performance. Three of such algorithms from ANN are Levenberg–Muarquardt Backpropagation (LMBP), Bayesian Programming (BP), and Conjugate Gradient (CG) algorithms. In this work, the expansive soil classified as A-7-6 group soil was treated with hydrated-lime activated rice husk ash (HARHA) in varying proportions between 0.1 and 12% by weight of soil at the rate of 0.1% to produce 121 datasets. These were used to predict the behavior of the soil’s strength parameters (CBR, UCS and R) utilizing the evolutionary hybrid algorithms of ANN. The predictor parameters were HARHA, liquid limit (wL), (plastic limit (wP), plasticity index (IP), optimum moisture content (wOMC), clay activity (AC), and (maximum dry density (δmax). A multiple linear regression (MLR) was also conducted on the datasets in addition to ANN to serve as a check and linear validation mechanism. MLR and ANN methods agreed in terms of performance and fit at the end of computing and iteration. However, the response validation on the predicted models showed a good correlation above 0.9 and a great performance index. Comparatively, the LMBP algorithm yielded an accurate estimation of the results in lesser iterations than the Bayesian and the CG algorithms, while the Bayesian technique produced the best result with the required number of iterations to minimize the error. And finally, the LMBP algorithm outclassed the other two algorithms in terms of the predicted models’ accuracy.
Application of ANFIS hybrids to predict coefficients of curvature and uniformity of treated unsaturated lateritic soil for sustainable earthworks
(Cleaner Materials, 2021) Onyelowe, Kennedy C.; Shakeri, Jamshid; Salahudeen, Bunyamin; Arinze, Emmanuel E.; Ugwu, Hyginus U.
Unsaturated lateritic soils are complex soils to work with due to moisture effects. So, the determination of its properties requires lots of time, labor and equipment. For this reason, the application of evolutionary learning techniques has been adopted to overcome these complexities. Lateritic soil under unsaturated condition classified as poorly graded and A‐7–6 group was subjected to treatment by using hybrid cement and nanostructured quarry fines in a stabilization method. The clay activity, clay content and frictional angle were determined through multiple experiments at different proportions of the additives. 121 datasets were collected through the multiple testing of treated specimens and 70% and 30% of the datasets were used in the model training and testing, respectively to predict the coefficients of curvature and uniformity (Cc and Cu) of the unsaturated lateritic soil. Fist, the multi‐linear regression (MLR) model showed that the selected input parameters correlated well with the output parameters. The model performance evaluation and validation selected indicators; R2, RMSE and MAE showed that ANFIS with 0.9999, 0.0021 and 0.0015 respectively, for the training and 0.9994, 0.0077 and 0.0059 respectively outclassed all its hybrid techniques and MLR in both training and testing. However, ANFIS‐PSO with performance indicators 0.9996, 0.0062 and 0.0050 respectively (training) and 0.9989, 0.0095 and 0.0073 respectively (testing); followed by ANFIS‐GA; 0.9991, 0.0094, and 0.0065 respectively (training) and 0.0089, 0.0099, and 0.0079 (testing) outclassed the other learning techniques for the Cc prediction model while ANFIS‐GA; 0.9949, 0.1000, and 0.0798 respectively (training) and 0.9954, 0.0983, and 0.0807 respectively, followed by ANFIS‐PSO; 0.9893, 0.1347, and 0.1011 respectively (training) and 0.9951, 0.1127, and 0.0924 respectively outclassed the other techniques for the Cu prediction model. Finally, ANFIS and its evolutionary hybrid techniques have shown their usefulness and flexibility in predicting stabilized unsaturated soil properties for sustainable earthwork design, construction and foundation performance monitoring.
Application of Gene Expression Programming to Evaluate Strength Characteristics of Hydrated-Lime-Activated Rice Husk Ash-Treated Expansive Soil
(Applied Computational Intelligence and Soft Computing, 2021) Onyelowe, Kennedy C.; Jalal, Fazal E.; Onyia, Michael E.; Onuoha, Ifeanyichukwu C.; Alaneme, George U.
Gene expression programming has been applied in this work to predict the California bearing ratio (CBR), unconfined compressive strength (UCS), and resistance value (R value or Rvalue) of expansive soil treated with an improved composites of rice husk ash. Pavement foundations suffer failures due to poor design and construction, poor materials handling and utilization, and management lapses. -e evolution of sustainable green materials and optimization and soft computing techniques have been deployed to improve on the deficiencies being suffered in the abovementioned areas of design and construction engineering. In this work, expansive soil classified as A-7-6 group soil was treated with hydrated-lime activated rice husk ash (HARHA) in an incremental proportion to produce 121 datasets, which were used to predict the behavior of the soil’s strength parameters utilizing the mutative and evolutionary algorithms of GEP. -e input parameters were HARHA, liquid limit (wL), (plastic limit (wP), plasticity index (IP), optimum moisture content (wOMC), clay activity (AC), and (maximum dry density (δmax) while CBR, UCS, and R value were the output parameters. A multiple linear regression (MLR) was also conducted on the datasets in addition to GEP to serve as a check mechanism. At the end of the computing and iterations, MLR and GEP optimization methods proposed three equations corresponding to the output parameters of the work. -e responses validation on the predicted models shows a good correlation above 0.9 and a great performance index. -e predicted models’ performance has shown that GEP soft computing has predicted models that can be used in the design of CBR, UCS, and R value for soils being used as foundation materials and being treated with admixtures as a binding component.
Computational Modeling of Desiccation Properties (CW, LS, and VS) of Waste-Based Activated Ash-Treated Black Cotton Soil for Sustainable Subgrade Using Artificial Neural Network, Gray-Wolf, and Moth-Flame Optimization Techniques
(Advances in Materials Science and Engineering, 2022) Onyelowe, Kennedy C.; Shakeri, Jamshid; Amini-Khoshalan, Hasel; Usungedo, Thompson F.; Alimoradi-Jazi, Mohammadreza
Artificial neural network (ANN), gray-wolf, and moth-flame optimization (GWO and MFO) techniques have been used in this research work to predict the effect of activated sawdust ash (ASDA) on the crack width (CW), linear shrinkage (LS), and volumetric shrinkage (VS) of a black cotton soil utilized as a subgrade material. Problematic soils or black cotton soils are not good pavement foundation materials except that they are pretreated in order to meet the basic strength characteristics required for roads in Nigeria. Due to this reason, there has been ongoing research to evaluate the best practices in which black cotton soils can be favorably utilized in earthwork construction. On the other hand, there is a huge concern on the solid waste management system in the wood processing environment and the recycling of sawdust into ash and its reuse as an alternative binder has offered a sustainable disposal system. +e work tries to use AI-based techniques to predict the crack and shrinkage behaviors of BCS treated with saw dust ash activated with alkali materials. +ere was appreciable improvement in the shrinkage and crack parameters over the 30-day drying period due to the addition of ASDA. +e intelligent model results showed that the three techniques successfully predicted the CW, LS, and VS with a performance accuracy above 90%, while ANN produced the minimal error in performance outperforming the other techniques. Sensitivity study showed that the drying time (T) was the most influential of the studied parameter. Hence, soil stabilization has shown its potential system of waste management in the wood processing industry.
Constrained vertex optimization and simulation of the unconfined compressive strength of geotextile reinforced soil for flexible pavement foundation construction
(Cleaner Engineering and Technology, 2021) Aju, Daniel E.; Onyelowe, Kennedy C.; Alaneme, George U.
Extreme vertex design (EVD) provides an efficient approach to mixture experiment design whereby the factor level possesses multiple dependencies expressed through component constraints formulation. Consequently, the derived experimental points are within the center edges and vertices of the feasible constrained region. EVD was deployed for the modeling of the mechanical properties of the problematic clayey soil-geogrid blends. Geogrids are geosynthetic materials which possess an open mesh-like structure and are mostly used for soil stabilization. The geotextile materials present a geosynthetic and permeable layer to support the soil and foundation by improvement of its stiffness characteristics and at a cheaper cost to procure compared to other construction materials and possess unique light weight properties with greater strength improvement on the soil layer when used. Minitab 18 and Design Expert statistical software were utilized for the mixture design experiment computation; to fully explore the constrained region of the simplex, I-optimal designs with a special cubic design model were utilized to formulate the mixture component ratios at ten experimental runs. I-optimality and Doptimality of 0.39093 and 1747.474, respectively, were obtained with G-efficiency of 64.8%. The generated laboratory responses were taken together with the mixture ingredients’ ratio and taken as the system database for the model development. Statistical influence and diagnostics tests carried out on the generated EVD model indicate a good correlation with the experimental results. Graphical and numerical optimizations were incorporated using a desirability functions that ranged from 0 to 1, which helped to arrive at the optimal combination of the mixture components. 0.2% of geogrid, 9.8% of water, and 90% of soil yielded the optimal solution with a response of 41.270 kN/m2 and a desirability score of 1.0. Model simulation was further carried out to test the model’s applicability with the results compared with the actual results using student’s t-test and analysis of variance. The statistical results showed p-value>.05 which indicates good correlation.
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.
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; Usungedo, Thompson; 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.
Hydraulic conductivity predictive model of RHA-ameliorated laterite for solving landfill liner leachate, soil and water contamination and carbon emission problems
(International Journal of Low-Carbon Technologies, 2022) Onyelowe, Kennedy C.; Ebid, Ahmed M; Baldovino, Jair de Jesús Arrieta; Onyia, Michael E.
The environment is seriously being affected by the leachate release at the unconstructed and badly constructed waste containment or landfill facilities around the globe. The worst hit is the developing world where there is little or totally no waste management system and facilities to receive waste released into the atmosphere. This research work is focused on the leachate drain into the soil and the underground water from landfills, which toxicifies both the soil and the water. Also, the construction of the liner or barrier with cement poses serious threat to the environment due to oxides of carbon release and this research also took this into account by replacing the utilization of cement with rice husk ash (RHA), which has proven to have the potentials of replacing cement as a supplementary binder. Laboratory tests were conducted to determine the hydraulic conductivity (K) of lateritic soil (LS) ameliorated with different dosages of RHA. Other hydromechanical properties of the treated blend were studied and multiple data were generated for the artificial neural network (ANN) back-propagation (-BP), genetic algorithm (GA) and gradual reducing gradient (GRG), genetic programming (GP) and evolutionary polynomial regression (EPR) prediction exercises. Results show that the LS was a poorly graded A-2 sandy silt soil, which was subjected to three different compaction energies with the minimum of the British standard light (BSL) and derived k of 6.95E-10, 50.75E-10 and 32.33E-10 for BSL, west African standard and British standard heavy, respectively. The RHA addition improved the studied properties of the ameliorated LS. Out of the five models, the ANN-GRG outclassed others with a performance of 99% with minimal error compared with the rest. Potentially, this research has shown that RHA with a pozzolanic chemical moduli of 81.47% can replace cement in the construction of ecofriendly and more efficient landfills and waste containemnt barriers to save the soil and the underground water as well as the environment from leachate contamination and carbon emissions.
Influence of moisture and geofluids (GF) on the morphology of quarry fines treated lateritic soil
(Cleaner Engineering and Technology, 2021) Onyelowe, Kennedy C.; Obianyo, Ifeyinwa I.
The influence of moisture migration in the form of GF on the morphology of lateritic soil has been studied with laboratory experiments. This was important due to the effect of adsorbed moisture during molding moisture addition in a stabilization protocol that gives rise to hydration. In addition, moisture adsorption and absorption play a very vital engineering role during the seasonal changes of wet and dry seasons when the water table rises and drops. This occurrence brings about alternate effects of wetting and drying of hydraulically bound structures like in pavement foundations. Therefore, it was pertinent to study how these changes affect soil microstructure to enable good design decisions. The soil used in this exercise was classified as highly plastic, poorly graded A-7-6 soil group according to AASHTO classification method. The soil was mixed with various proportions of quarry fines under different molding moisture conditions and the specimens were prepared for scanning electron microscopy (SEM) exposure. The results of the SEM exposure showed that GF applied here as molding moisture improve the agglomeration of treated soil particles to form flocs in a stabilization process. However, the microspores and crack propagation were observed more in the structure with less amount of quarry fine, i. e., at 2% QF than that at 4% QF. This showed the pozzolanic effect of QF on soil under the influence of GF. GF in all its forms should be studied for sustainable earthwork design and construction.
Intelligent prediction of coefficients of curvature and uniformity of hybrid cement modified unsaturated soil with NQF inclusion
(Cleaner Engineering and Technology, 2021) Onyelowe, Kennedy C.; Shakeri, Jamshid
The cost and sophisticated equipment required to conduct earthwork laboratory experiments have been of concern to the design and performance monitoring of infrastructures in recent times. Lateritic soils especially those under unsaturated conditions are erratic and deserve close attention in terms of laboratory studies. In order to overcome the rigors and time consumed during experimental procedures, soft computing has been used to predict soil parameters for the purpose of design and construction. In this work, the ANN, GEP and LMR were employed to predict the coefficients of curvature and uniformity of lateritic soil treated with multiple binders locally generated, which were hybrid cement (HC) and nanostructured quarry fines (NQF). The effect of the varying dosages of HC and NQF added to the soil were studied and the behavior of clay activity, clay content, frictional angle, coefficients of curvature and uniformity were measure. 121 datasets were generated from the experimental exercise for the selected parameter both for the predictors and for the targets. These datasets were deployed in the ratio of 70 is to 30% for training and testing of the models predictions respectively. The performances of the models were evaluated using error analysis (VAF, RMSE, MAE) and accuracy (R2) indices and it was observed that the ANN outclassed both GEP and LMR due to its speed and robustness in adopting backpropagation and feed-forward algorithms. Furthermore, the sensitivity analysis showed that F, C, H (HC), NQF and Ac in that order of most influential to least influential influenced the behavior of the Cc model with H (HC) and NQF showing equal effect on the Cc. Also, H (HC), NQF, F, C and Ac in that order of influence from most to least affected the behavior of the Cu predicted model also with HC and NQF having equal effect on the Cu. Generally, the learning techniques showed good performance in predicting the outputs hence are good techniques to be utilized in design and performance evaluation.
Morphology and mineralogy of rice husk ash treated soil for green and sustainable landfill liner construction
(Cleaner Materials, 2021) Onyelowe, Kennedy C.; Obianyo, Ifeyinwa I.; Onwualu, Azikiwe P.; Onyia, Michael E.; Moses, Chima
The morphology and mineralogy of the soil treated with rice husk ash (RHA) under different molding moisture conditions. Leachate condition in landfills built with compacted clay soil is damaging the underground water flow with the hazards released from disposed and decomposing waste materials. This makes landfills dangerous infrastructure. The leakage can be dealt with through the deployment of green materials developed from agricultural waste. One of such wastes is rice husk combusted to derive ash. The test soil used in this exercise has been classified as highly plastic and poorly graded. The treated soil was examined by scanning electron microscopy and x‐ray diffractometer methods. From the test results, the presence of goethite alongside quartz and kaolinite were observed in the XRD (X‐ray Diffraction) spectra of 6% and 10 % RHA treated soil. The Goethite possessing an inner needle‐like structure with a closed packed striated structure makes the composite a promising material for constructing landfill liners. This is because the closed packed striated structure of the goethite present in the composite will slow down the vertical seepage of leachate to allow its collection and removal by the leachate collection system. The composite will form a barrier between groundwater, soil, and substrata, and waste. From the SEM (Scanning Electron Microscopy), the uniformly distributed grain boundaries and smaller grain size of the composite (lateritic soil and RHA) will serve as a barrier to the movement of contaminants and other leachates to the groundwater and thus, making the composite a viable material for landfill liner system.
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.
Pozzolanic Reaction in Clayey Soils for Stabilization Purposes: A Classical Overview of Sustainable Transport Geotechnics
(Advances in Materials Science and Engineering, 2021) Onyelowe, Kennedy C.; Onyia, Michael E.; Bui Van, Duc; Baykara, Haci; Ugwu, Hyginus U.
Problematic soil stabilization processes involve the application of binders to improve the engineering properties of the soil. /is is done to change the undesirable properties of these soils to meet basic design standards. However, very little attention has been given to the reactive phase of soil stabilization. /is phase is the most important in every stabilization protocol because it embodies the reactions that lead to the bonding of the dispersed particles of clayey soil. Hence, this reactive phase is reviewed. When clayey soils which make up the greatest fraction of expansive soil come in contact with moisture, they experience volume changes due to adsorbed moisture that forms films of double diffused layer on the particles. When this happens, the clayey particles disperse and float, increasing the pore spaces or voids that exist in the soil mass. Stabilizations of these soils are conducted to close the gaps between the dispersed clayey soil particles. /is is achieved by mixing additives that will release calcium, aluminum, silicon, etc., in the presence of adsorbed moisture, and a hydration reaction occurs. /is is followed by the displacement reaction based on the metallic order in the electrochemical series. /is causes a calcination reaction, a process whereby calcium displaces the hydrogen ions of the dipole adsorbed moisture and displaces the sodium ion responsible for the swelling potential of clayey soils. /ese whole processes lead to a pozzolanic reaction, which finally forms calcium alumina-silica hydrate. /is formation is responsible for soil stabilization.
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 "Onyelowe, Kennedy C."

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