Prediction of Compressive Strength using Genetic Programming Involving NDT Results

Kumar, Prasanta and Kumar, Ankit (2015) Prediction of Compressive Strength using Genetic Programming Involving NDT Results. BTech thesis.



Compressive strength of concrete is major parameter to assess the overall quality of concrete as other mechanical prosperities are directly related to the compressive strength. It can be determined using the destructive (DT) and non-destructive testing (NDT) methods. The destructive testing method is carried out by crushing the specimen to failure while the non-destructive is carried out without destroying the concrete specimen. The destructive method is time taking process and required equipment’s and power. Whereas the NDT methods like the rebound (Schmitz) hammer and Ultrasonic Pulse velocity (UPV) are most popular because they are handy, quicker and easy to use. Though the NDT methods are much quicker; their values are more of an approximation than exact compressive strength values. They are also machine specific, hence a calibration curve is provided by supplier which may not be reliable. The Indian code recommends about 25% variation in results, which is very high. The newly developed soft computing techniques like ANN, Fuzzy logic, Genetic programming etc. may be used to prepare a better numerical model correlating DT and NDT results. Hence the aim of the present study is to propose a model correlating the compressive strength obtained from destructive and non-destructive methods by using Genetic Programming. The whole work involves casting of 100 cubes of 150mm size belonging to of different grades of concrete. They were tested under compression following DT and NDT methods. These data were used for modelling ie.(70% for training and 30% for testing ) in GP. The modelling is done two ways, first by using variables as weight and Rebound values and secondly by using weight, rebound values and UPV values. The models obtained were found to be in good agreement with actual values imparting 6.744 % and 7.4434% error respectively.

Item Type:Thesis (BTech)
Uncontrolled Keywords:Genetic programming,rebound hammer,UPV,ANN,NDT
Subjects:Engineering and Technology > Civil Engineering > Structural Engineering
Divisions: Engineering and Technology > Department of Civil Engineering
ID Code:6859
Deposited By:Mr. Sanat Kumar Behera
Deposited On:06 Jan 2016 17:57
Last Modified:06 Jan 2016 17:57
Supervisor(s):Patel, S

Repository Staff Only: item control page