http://localhost:8080/xmlui/handle/123456789/449 2026-06-23T06:17:19Z http://localhost:8080/xmlui/handle/123456789/2824 Title: Prediction of Strength of Remixed Concrete by Application of Orthogonal Decomposition, Neural Analysis and Regression Analysis Authors: Bidkar, K. L.; Jadhao, P. D. Abstract: Compressive strength is the foremost property of concrete which is in uenced by a number of parameters. These parameters plays important role for the characteristics achieved by concrete. Orthogonal decomposition, neural analysis and regression analysis tools can be utilized where the dependence and independence of these parameters to be considered. In this paper these analyses are considered for remix concrete, in which apart from the cement contents, w/c ratio, proportions of C.A., F.A., the other parameters like blend ratio (r=Qo/Qf , Qo=quantity of old partially set concrete, Qf =quantity of fresh concrete) time lag ( time between preparation and placing of concrete) also plays the important role 2019-06-02T00:00:00Z http://localhost:8080/xmlui/handle/123456789/2822 Title: Mathematical modeling for predication of strength of remixed concrete. Authors: Bidkar, K. L.; Jadhao, P. D. Abstract: This paper deals with the methodology, related to application of mathematical model for reuse of partially set old concrete by adding fresh concrete to form serviceable mix by considering their time lags and blend ratios. As Compared to the strength of the freshly prepared concretes the preset concrete obviously gives the reduction in strength. This reduction is further possible to be minimized to a certain extent on blending some quantity of a relatively fresh mix to the existing quantity of the preset mix. In the statistical methods of concrete mix design in civil engineering the most frequently used models are Scheffe’s and Osadebe’s models, which are quite suitable for concrete mix optimization but are greatly limited as a predetermined number of experiments must be carried out in order to formulate them and they can only be applied for mix ratios that fall within the predetermined observation points. Ibearugbulem’s regression model has been formulated as a new model to take care of these inherent problems in Scheffe’s and Osadebe’s. Some modifications were made to obtain the new model. This new model has been tested on concrete cubes for different mix ratios for 28 days compressive strengths. The Fisher f-test shows that the values of compressive cube strength predicted by the new regression model are very close to those from the experiment strength values, with f-value of 3.44 at 95% confidence level. Hence this new model of regression is useful in concrete mix desig 2019-07-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/2614 Title: Environmental Geotechnology Authors: Agnihotri, A.K.; Krishna, R.R; Bansal, A. Abstract: The world in the twenty-first century is posed with unprecedented challenges such as rapid increase in world population, global resource depletion, increased waste generation, and increased greenhouse gas emissions, the consequences of which are unnerving. This, in fact, has triggered the geotechnical and geoenvironmental engineers to develop sustainable geosystems for civil infrastructure and for the protection of the environment. However, the solutions to these challenges are interdisciplinary. For geotechnical considerations, engineering properties of the soil and rock are influenced by several geochemical parameters and geochemical processes, which are usually not addressed in-depth by geotechnical engineers. However, it is crucial to understand these geochemical aspects of the soil and its environment so that they can be engineered to create favorable conditions for developing resilient and sustainable geosystems. This paper first presents the most significant geochemical properties and processes of soils, rock, and groundwater, followed by a discussion of recent advances that demonstrate the significance of geochemical processes toward an understanding and development of effective and potentially sustainable geosystems. The paper emphasizes on the need for studying the geochemistry and the geochemical factors affecting the performance and behavior of a geosystem. 2019-09-16T00:00:00Z http://localhost:8080/xmlui/handle/123456789/2489 Title: Prediction of Strengths of Remixed Concrete Authors: Bidkar, K L; Jadhao, P. D. Abstract: This paper deals with the methodology, related to application of neural network analysis for reuse of partially set old concrete by adding fresh concrete to form usable mix by considering their time lags and blend ratios. When we relate the strength of the freshly prepared concretes the preset concrete obviously gives the reduction in strength. This problem will be overcome by adding a specific quantity of fresh mass to the partially set old concrete mass. The paper focuses on the utilization of neural network (N.N.) for predicting the 28-day strengths of concrete. The complex nonlinear relationship between the responses (factors that influence concrete strength-blend ratio, time lag, strength at initial setting time and final setting time) and the output (concrete strength) can be built by applying N.N. High degree of accuracy is achieved by the model for prediction of strengths. 2019-06-12T00:00:00Z