Analytically Determining Bond Shear Strength of Fully Grouted Rock Bolt Based on Pullout Test Results

Usually, in a fully grouted rock bolt pullout test the load-displacement curve of the rock bolt head is recorded. This paper presents an analytical method to use this curve for determining the bond (bolt-grout and grout-rock interface) shear strength parameters. For this purpose, the fully grouted rock bolt interaction with grout and surrounding rock in the pullout test is investigated and the load-displacement curve of the bolt head (beginning of the bonded section) is obtained analytically. For modeling the bolt-grout interface behavior a distribution of the shear stress along the fully grouted rock bolt by consideration of bolt shank failure is used. In this regard, different stages including complete bonding, partial decoupling, decoupling with the residual shear strength and complete decoupling are considered. With increasing the applied load, two possible cases involving the rock bolt complete pullout and bolt shank yielding are taken into account. Based on the presented analytical method, the obtained bolt head load-displacement curve can be compared with the one recorded in the pullout test. With this, the relevance of selected shear strength parameters compared to real parameters can be assessed. A flowchart for determining the bolt bond shear strength parameters is presented using the trial and error method (coded in Matlab). The proposed solution is used to determine two experimental pullout shear strength parameters. The results show good agreement between predicted and calculated load-displacement curves

Overbreak prediction and optimization in tunnel using neural network and bee colony techniques

Overbreak is an undesirable phenomenon in blasting operations. The causing factors of overbreak can be generally divided as blasting and geological parameters. Due to multiplicity of effective parameters and complexity of interactions among these parameters, empirical methods may not be fully appropriated for blasting pattern design. In this research, artificial neural network (ANN) as a powerful tool for solving such complicated problems is developed to predict overbreak induced by blasting operations in the Gardaneh Rokh tunnel, Iran. To develop an ANN model, an established database comprising of 255 datasets has been utilized. A three-layer ANN was found as an optimum model for prediction of overbreak. The coefficient of determination (R2) and root mean square error (RMSE) values of the selected model were obtained as 0.921, 0.4820, 0.923 and 0.4277 for training and testing, respectively, which demonstrate a high capability of ANN in predicting overbreak. After selecting the best model, the selected model was used for optimization purpose using artificial bee colony (ABC) algorithm as one of the most powerful optimization algorithms. Considering this point that overbreak is one of the main problems in tunneling, reducing its amount causes to have a good tunneling operation. After making several models of optimization and variations in its weights, the optimum amount for the extra drilling was 1.63 m2, which is 47% lower than the lowest value (3.055 m2). It can be concluded that ABC algorithm can be introduced as a new optimizing algorithm to minimize overbreak induced by tunneling