Anticipating the Compressive Strength of Hydrated Lime Cement Concrete Using Artificial Neural Network Model

Abstract

In this research work, the levernberg Marquardt back propagation neural network was adequately trained to understand the relationship between the 28th day compressive strength values of hydrated lime cement concrete and their corresponding mix ratios with respect to curing age. Data used for the study were generated experimentally. A total of a hundred and fourteen (114) training data set were presented to the network. Eighty (80) of these were used for training the network, seventeen (17) were used for validation, and another seventeen (17) were used for testing the network's performance. Six (6) data set were left out and later used to test the adequacy of the network predictions. The outcome of results of the created network was close to that of the experimental efforts. The lowest and highest correlation coefficient recorded for all data samples used for developing the network were 0.901 and 0.984 for the test and training samples respectively. These values were close to 1. T-value obtained from the adequacy test carried out between experimental and model generated data was 1.437. This is less than 2.064, which is the T values from statistical table at 95% confidence limit. These results proved that the network made reliable predictions. Maximum compressive strength achieved from experimental works was 30.83N/mm2 at a water-cement ratio of 0.562 and a percentage replacement of ordinary portland cement with hydrated lime of 18.75%. Generally, for hydrated lime to be used in making structural concrete, ordinary portland cement percentage replacement with hydrated lime must not be up to 30%. With the use of the developed artificial neural network model, mix design procedure for hydrated lime cement concrete can be carried out with lesser time and energy requirements, when compared to the traditional method. This is because, the need to prepare trial mixes that will be cured, and tested in the laboratory, will no longer be required.

Comparative Study on Mechanical Properties of Concrete Blended with Costus englerianus Bagasse Ash and Bagasse Fibre as Partial Replacement for Lime and Cement

“…is implied that cement is a better binding material for concrete compared to hydrated lime. Previous investigations on the performance of cement and hydrated lime concrete or mortar also showed that the compressive strength of cement concrete performed better than lime concrete [14,15,18], which was attributed to the slow rate of the hydration process in lime concrete [15].…”

“…Lime, as binding material, yields concrete with low strength, which may not be useful in certain areas of construction [13][14][15][16][17]. In a study by Salman and Muttar [18], the optimum compressive strength of Portland cement concrete obtained at 28 days of curing was 26.96 N/mm 2 , but just 6.12 N/mm 2 was recorded at 28 days of curing for lime concrete and only increased by 50% (13.15 N/mm 2 ) when the curing period was increased to 90 days.…”

“…In a study by Salman and Muttar [18], the optimum compressive strength of Portland cement concrete obtained at 28 days of curing was 26.96 N/mm 2 , but just 6.12 N/mm 2 was recorded at 28 days of curing for lime concrete and only increased by 50% (13.15 N/mm 2 ) when the curing period was increased to 90 days. However, Awodiji et al [14] recommended the addition of pozzolanic materials in lime concrete to increase the lime concrete strength designed for construction purposes. Brzyski [19] reported an increase in the strength of concrete by adding 10% metakaolinite, micro silica, and zeolite, independently, in lime concrete, which doubled the strength by 20%.…”

Prediction of Fresh and Hardened Properties of Concrete Containing Nanostructured Cassava Peel Ash Using Ibearugbulem's Approach

“…Concrete is a composite material constituting cement, water, fine aggregates, and coarse aggregates in a calculated mix measure. It is globally the most-used construction material with its increasing demand for infrastructural development in both developing and developed countries [1]. The availability of its constituents determines its overall production cost.…”

Prediction of Compressive Strength of Self-Compacting Concrete (SCC) with Silica Fume Using Neural Networks Models

“…This potential of ANN has been harnessed for wide applications in the field of civil engineering. ANN was used to estimate the main parameters needed in the design of concrete such as the compressive strength of hydrated lime cement concrete [11]. ANN was also used to evaluate the sulphate expansion of different types of cement using water/binder, cement content, FA or SF, C3A, and exposure duration as input parameters [12].…”