Application of Gene Expression Programming to Evaluate Strength Characteristics of Hydrated-Lime-Activated Rice Husk Ash-Treated Expansive Soil

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. The 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. The 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. The responses validation on the predicted models shows a good correlation above 0.9 and a great performance index. The 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

Artificial intelligence prediction model for swelling potential of soil and quicklime activated rice husk ash blend for sustainable construction

Artificial intelligence (AI) algorithms of adaptive neuro-fuzzy inference system or the adaptive network-based fuzzy inference system (ANFIS) has been deployed to predict the swelling potential (SP) of treated weak soil. The soil was treated with quicklime activated rice husk ash (QARHA) and the prediction efficiency was compared with the previous outcomes of this operation from literature. The need for effective utilization of construction materials to achieve sustainable designs and monitoring of the behavior of built environment is the motivation behind the deployment of artificial intelligence in geo-environmental research and field operations. The use of ANFIS is common in different fields of science and business to predict the best fits from several data points. The results of this modeling exercise conducted with 25 datasets from mixture experimental treatment of soft soil with QARHA has shown that ANFIS is a better tool compared to the individual algorithms of ANN and FL and even the other artificial intelligence tools like scheffe, ANOVA, regression and extreme vertices methods. With performance index of 88% and correlation of about 71% in the ANFIS testing and 17% and 99% respectively in the ANFIS training, ANFIS proved to be a more powerful tool in achieving a more sustainable material utilization in earthwork constructions, design and monitoring of geotechnical systems performance

Numerical Analysis and Parametric Study on Multiple Degrees-of-Freedom Frames

The design of multiple degrees-of-freedom frames is critical in civil engineering, as these structures are commonly used in various applications such as buildings, bridges, and industrial structures. In this study, a six-degrees-of-freedom beam-column element stiffness matrix was formulated by superposition of beam and truss elements stiffness matrices and was adapted to statically analyze indeterminate frame structures. The development of a numerical model for the frame structures was achieved using the finite element method in the current study. Also, the investigation of the effects of various parameters such as frame geometries, material properties, and loading conditions was conducted on the internal forces developed in the frame structures. Three different parametric study cases that presented the frame structures with varying geometries and loading conditions were analyzed utilizing this matrix approach for the sake of emphasis and to evaluate the flexibility and adequacy of this formula to analyze the indeterminate frames using the MATLAB software. The analysis method comprised the derivation of the system displacements employing the relationships between the stiffness matrix and fixed end forces as the force vector and taking the attained displacements, which would be transformed to the local coordinates to obtain the member forces. The computed results from the element stiffness matrix approach were further statistically compared with the results achieved from the finite element software (SAP2000) applying the analysis of variance (ANOVA). The statistical results showed a P-value > 0.05, which indicated a good correlation between the compared results and adequate performance for the derived beam-column element matrix formula method.