Estimating the Shear Strength of Binary Blended Concrete Incorporated with Hydrated Lime via Artificial Intelligence Technique | Chapter 6 | Engineering Research: Perspectives on Recent Advances Vol. 1

This analytical study examined the shear strength of blended Portland cement concrete with hydrated lime (HL) added as an additive. For a variety of mix ratios, 120 shear strength values were empirically acquired at 7, 14, 21, and 28 days. The ingredients of this concrete were water, portland cement (PC), HL, granite chips (GC), and river sand (RS). 96 of the findings were used to create a Levernberg-Marquardt backpropagation artificial neural network (ANN) for measuring the concrete's shear strength. The 24 outcomes that were not employed were utilized to test the forecast's efficacy of the ANN. The six input variables in the model were the proportions of water, curing age, PC, HL, RS, and GC. The measured value of the shear strength was the output variable. One hidden layer comprising 20 neurons was implemented. The highest 28-day shear strength value of 1.257 N/mm2 was recorded when 13.75% of PC was supplanted with HL for a water-to-cement ratio of 0.58. The ANN's performance demonstrated that the model was implemented well enough. The network forecast and experimental values yielded root mean square errors (RMSE) ranging from 0.0278 to 0.06536. These are nearly equal to zero. Furthermore, the calculated factor of agreement (IA) was found to be between 0.0475 and 0.1747. These are within the predetermined range of 0 to 1 for varied consistency. R-values for the training, validating, testing and for all data were 0.96978, 0.96303, 0.95739, and 0.96624 accordingly. These were all close to 1 meaning that the model fits very well with the data sets. The most significant average percentage error between the experimental outcomes and the forecasts made by the model was calculated to be 2.5066%. Finally, there is no longer a requirement for experimental laboratory study because the developed ANN can be utilized to forecast the shear strength of hydrated lime cement concrete with convincing accuracy thereby saving time and energy during the concrete mix design process.

 

Author (s) Details

 

C.T.G. Awodiji
Department of Civil and Environmental Engineering, University of Port-Harcourt, Nigeria.

 

D.O. Onwuka
Department of Civil Engineering, Federal University of Technology, Owerri, Nigeria.

S. Sule
Department of Civil and Environmental Engineering, University of Port-Harcourt, Nigeria.

 

Please see the book here:- https://doi.org/10.9734/bpi/erpra/v1/2978

Sand Columns: An Effective Improvement Technique for Soft Soils| Chapter 4 | Advanced Aspects of Engineering Research Vol. 14

 Sand Columns (SC), also known as Sand Compaction Piles (SCP), are increasingly widely utilised to reinforce a variety of soft soils. Sand columns increase soft soil's ultimate bearing capacity, speed up stabilisation, prevent liquefaction in loose sandy soils, and provide lateral resistance to horizontal movement. The purpose of this research is to build small-scale laboratory models to evaluate the impact of floating columns on clayey soil with silty deposits. The laboratory tests were conducted on a circular column with a diameter of 37 mm, and the results of the tests were published. Ground that had been treated and ground that had not been treated were compared. Sand columns' impacts on soils with varied shear strengths (low-medium-high), slenderness ratios (L/D), and loading patterns are investigated. The group effect was further explored by altering the space between the columns. The axial ability of the sand column was measured by loading the column area alone, and the stiffness of composite ground was computed by loading the full area of the test model. A sand column will greatly improve the engineering features of soft clayey soil, according to the findings of the current investigation. The group effect was researched as well, and it was determined that increasing the distance between the columns decreases group efficiency. The axial potential of the sand columns diminishes as the distance between the columns increases.

Author (s) Details

Dr. Sohaib Naseer
Department of Geotechnical Engineering, National University of Sciences and Technology (NUST) Islamabad, Pakistan.

View Book :  https://stm.bookpi.org/AAER-V14/article/view/1263