Browsing by Author "Jalal, Fazal E."

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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.
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    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.
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    Isolated Effect and Sensitivity of Agricultural and Industrial Waste Ca-Based Stabilizer Materials (CSMs) in Evaluating Swell Shrink Nature of Palygorskite-Rich Clays
    (Advances in Civil Engineering, 2021) Jalal, Fazal E.; Jamhiri, Babak; Naseem, Ahsan; Hussain, Muhammad; Iqbal, Mudassir; Onyelowe, Kennedy
    *is paper evaluates the suitability of sugarcane bagasse ash (SCBA) and waste marble dust (WMD) on the geotechnical properties of Palygorskite-rich expansive clays located in northwest Pakistan. *ese problematic soils exhibit undesirable characteristics which greatly affect the pavements, boundary walls, slab-on-grade members, and other civil engineering infrastructures. A series of geotechnical tests were performed on soil specimens using prescribed percentages of the aforementioned Ca-based stabilizer materials (CSMs). *e investigation includes X-Ray Diffraction (XRD) Analysis, Scanning Electron Microscopy (SEM), X-Ray Fluorescence (XRF) tests, and physicomechanical properties such as moisture-density relationship, Atterberg’s limits, swell pressure, and an ANN-based sensitivity analyses of overall swell pressure development. *e outcomes of these experimental investigations showed that the addition of CSMs into the expansive soils increased to 4% SCBA and 10% WMD, the plasticity index reduced by 30% and 49%, the volumetric swell decreased from approximately 49% to 86% and 63%, and the swelling pressure reduction was from 189 kPa to 120 kPa and 160 kPa (about 15% and 36%), respectively. It is interesting to note that replacement with specified CSM accelerated the strength of soil at extended curing periods and the optimum improvement in the strength behavior of the soil was also recorded. Moreover, with addition of the respective CSMs, the compactability and strength characteristics were ameliorated, while plasticity was significantly lowered. Given the amount of SCBA and WMD produced annually, their utilization for the stabilization of problematic soils, even in relatively low concentrations, could potentially have a substantial impact on the sustainable reuse of these waste materials.
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    Smart computing models of California bearing ratio, unconfined compressive strength, and resistance value of activated ash‑modified soft clay soil with adaptive neuro‑fuzzy inference system and ensemble random forest regression techniques
    (Multiscale and Multidisciplinary Modeling, Experiments and Design, 2021) Iqbal, Mudassir; Onyelowe, Kennedy C.; Jalal, Fazal E.
    Sugeno or Takagi–Sugeno–Kang (TSK) type fuzzy inference system ANFIS proposed by Jang and ensemble random forest (ERF) regression, an extension of bootstrap aggregation of decision trees, has been employed to forecast the triple targets of strength properties of a hydrated-lime activated rice husk ash stabilized soft clay soil. This was necessitated to deal with the incessant failure being recorded on flexible pavements around the world and the efforts being made to tackle the situation in a more smart and sustainable approach. The independent variables of this model protocol were the HARHA—hydratedlime- activated rice husk ash, w L—liquid limit, w P—plastic limit, I P—plasticity index, w OMC—optimum moisture content, A C—clay activity, max—maximum dry density, while CBR—California bearing ratio, UCS 28—unconfined compressive strength at 28 days curing and R—resistance value were estimated and employed as the targets (dependent variables). The natural clayey expansive soil used for this research work was investigated through preliminary experiments and classified as A-7–6 group according to AASHTO. It exhibits a very high plasticity index with high clay content, hence needed modification to be rendered as a foundation material. The soil was treated with varying percentages of HARHA, and the effect on the consistency limits, compaction, CBR, UCS, and R-value was studied. These observed values gave rise to 61 datasets. The observed datasets were deployed on the learning capacity of ANFIS and ERF regression to proposed models for the targets. The outcome of the results showed that both the models presented a close correlation between the parameters used in the model execution. Evaluation of the models was performed using a variety of statistical errors, Kendall and Spearman’s rank correlations. The results of ERF regression outclasses ANFIS model yielding a 100% coefficient of determination (R) for the triple targets. The performance evaluation and validation tests show that the coefficient of determination was more than 0.94 with minimized errors. It was concluded that ERF regression and ANFIS learning techniques are viable smart approaches to forecasting engineering problems for a more sustainable design and performance evaluation.