Superiority of artificial neural networks over statistical methods in prediction of the optimal length of rock bolts

Rock bolting is one of the most important support systems used for rock structures. Rock bolts are widely used in underground excavations as they are suitable for a wide range of geological conditions and allow using progressive design methods; besides, they help economising in the use of materials and manpower. Thus, to provide the most effective support at minimum cost by means of rock bolting, it is essential to optimise the elements contributing to bolt design, including their length, as well as bolt density and tension during installation. This paper considers the length of bolts for optimisation of the design phase, which is one of the most important parameters impacting the entire design procedure. Presenting and comparing results of some statistical models, neural network modeling is introduced as powerful means in prediction of the optimal length of rock bolts. Subsequent to training and testing of a large number of 1-layer and 2-layer backpropagation neural networks, it was reported that the optimal model was the network with the architecture of 6-18-3-1 as it demonstrated the minimum RMSE and MAE as well as the maximum R2. In comparison to statistical models (0.7182 for the value of R2 in the multiple linear regression model, 0.68 in the polynomial model and 0.7 in the dimensionless model), the results obtained by the neural network modeling – i.e. the coefficient of determination R2 of 0.9259, the value of mean absolute error MAE of 0.068, and the root mean squared error RMSE of 0.078 – not only proved their superiority but also introduced the neural network modelling as a highly capable prediction tool in forecasting the optimal length of rock bolts. Furthermore, sensitivity analysis was used to obtain parameters that have the greatest and the least impact on the optimal bolt length: the effect of the overburden thickness, tensile strength, cohesion and Poisson's ratio on the optimal bolt length was almost the same while the friction angle had the least influence

Evaluating elastic-plastic behaviour of rock materials using hoek–brown failure criterion

As a matter of fact, the failure criteria only predict failure's initiation in materials. And, in order to predict post-yield behaviour of materials, a much complicated formulation for stress-strain relationship is required, which we know as plasticity theory. For instance, these formulations are developed based on Mohr-Coulomb criterion for soils and Drucker-Prager criterion for concrete. According to a majority of rock mechanics researchers, the empirical and experimental Hoek-Brown failure criterion is one of the well-progressed and suitable criteria, which can efficiently predict the rock failure initiation under different stress states for various types of intact rocks and rock masses. In this article, according to the suggestion by Heok explained in his paper of 1997, this rugged mentioned criterion is considered as a yield criterion and the elastic-perfect plastic behaviour of rock masses is determined using calculating material constitutive matrix's arrays in terms of Hoek-Brown's material constants and mechanical characteristics of rock materials in the general stress space, considering associated flow rule

Compressive strength of sandy soils stabilized with alkali-activated volcanic ash and slag

In recent years, compared with the traditional portland cement, environmentally friendly geopolymers have gained more attention as construction materials. This paper considered volcanic ash (VA) and ground granulated blast furnace slag (GGBFS) in different percentages (0%, 3%, 7%, and 10%) as a replacement for the conventionally used portland cement to stabilize sandy soils. NaOH and Na2SiO3 in different concentrations (4, 8, and 12 M) and alkali to binder ratios (1, 1.5, 2, and 3) were used as alkali activator solutions to build new geopolymers. Samples were cured at both ambient and oven temperatures and for 1, 7, and 28 days. Unconfined compressive strength (UCS) of samples then was evaluated. Two predictive approaches, artificial neural network (ANN) modeling and the evolutionary polynomial regression technique (EPR), were applied to model UCS of geopolymerized sand samples. Regarding the high value of the coefficient of determination of the proposed ANN, 97%, and acceptable prediction errors, RMS error of 0.0439 and MAE of 0.0336, an 8-5-10-1 ANN was introduced as a more accurate tool for the prediction of UCS. Next, three-dimensional parametrical studies investigated the effects of simultaneous changes in alkali solution, binder, and curing condition parameters on UCS values of geopolymerized samples. Sensitivity analysis based on the cosine amplitude method introduced the Si/Al ratio as the parameter most affecting and VA content as the parameter least affecting the compressive strength of samples. Results were analyzed further using pH and electrical conductivity tests and interpreted based on microstructural investigations using scanning electron microscopy (SEM) images and X-ray diffraction analysis

A novel solution for ground reaction curve of tunnels in elastoplastic strain softening rock masses

Ground Reaction Curve (GRC) is one of the most important elements of convergence-confinement method generally used to design tunnels. Realistic presentation of GRC is usually assessed based on the advanced rock strength criteria, also, rock mass behavior (including plasticity and softening treatments). Since taking these parameters into ac­count is not simply possible for practitioners and needs complicated coupled theoretical-numerical solutions, this paper presents a simple novel approach based on Evolutionary Polynomial Regression to determine GRC of rock masses obeying both Mohr-Coulomb and Hoek-Brown criteria and strain softening behaviors. The proposed models accurately present support pressures based on radial displacement, rock mass strength and softening parameter (determination coefficient of 97.98% and 94.2% respectively for Mohr-Coulomb and Hoek-Brown strain softening materials). The ac­curacy of the proposed equations are approved through comparing the EPR developed GRCs with the ground reaction curves available in the literature. Besides, the sensitivity analysis is carried out and in-situ stress, residual Hoek-Brown’s m constant and residual dilation angle are introduced as parameters with the most influence on the support pressure in Hoek-Brown and peak and residual geological strength index are the most affective parameters on the support pressure of tunnels in the strain softening Mohr-Coulomb rock mass

Neural Prediction of Tunnels’ Support Pressure in Elasto-Plastic, Strain-Softening Rock Mass

The prediction of the support pressure (Pi) and the development of the ground reaction curve (GRC) are crucial elements of the convergence–confinement procedure used to design underground structures. In this paper, two different types of artificial neural networks (ANNs) are used to predict the Pi of circular tunnels in elasto-plastic, strain-softening rock mass. The developed ANNs consider the stress state, the radial displacement of tunnel and the material softening behavior. Among these parameters, strain softening is the parameter of the deterioration of the material’s strength in the plastic zone. The analysis also presents separate solutions for the Mohr–Coulomb and Hoek–Brown strength criteria. In this regard, multi-layer perceptron (MLP) and radial basis function (RBF) ANNs were successfully applied. MLP with the architectures of 15-5-10-1 for the Mohr–Coulomb criteria and 17-5-15-1 for the Hoek–Brown criteria appeared optimum for the prediction of the Pi. On the other hand, the RBF networks with the architectures of 15-5-1 for the Mohr–Coulomb criterion and 17-3-12-1 for the Hoek–Brown criterion were found to be the optimum for the prediction of the Pi

Buckling of the Steel Liners of Underground Road Structures: the Sensitivity Analysis of Geometrical Parameters

Designing a suitable, applicable and efficient support system for underground road structures have always been one of the most important engineering tasks for tunnel engineers. There are some different support systems applied to making underground structures safe against overburden and lateral pressures. Among these systems, permanent or temporary steel frames, wire meshes, rock bolts and shotcretes have been commonly used for suffering the exerted burdens and making the structure a safe place. This paper proposes a numerical analysis of the geometrical instability of steel-arch shells as one of the main bodies of underground road structure liners by means of calculating their buckling load and utilizing the finite element method. In this regard, a considerable number of structures (84) having different geometrical parameters have been modelled and their buckling loads have been calculated. For this purpose, the thickness, internal angle and radius of the periphery cylinder of the arch-shell system were considered taking into account geometrical parameters. Moreover, to accurately model the buckling load using the proposed algorithm, the weight of the structure has also been included in the made calculations. Finally, as the main scope is based on the Cosine Amplitude Method, sensitivity analysis is carried out to investigate the strength of the relationship between each input geometrical parameter and their buckling load. Based on the obtained relationships, the thickness of the structure is reported as the most affective geometrical parameter on buckling steel arch-shell support systems. In addition, the internal angle of arch supports is the least influential parameter

Stabilization of Problematic Silty Sands Using Microsilica and Lime

In this study, to stabilize problematic silty sand soils, Microsilica-Lime admixture was used as an additive. Various samples containing 0, 1, 2, 5, 10 and 15% (by weight) Microsilica and 0, 1, 3 and 5% (by weight) Lime were prepared. To investigate the role of the studied additives on the stabilization of the sandy soils, unconfined compressive strength of the materials and their swelling potential, were considered. To do this, unconfined compressive strength test, California Bearing Ratio, also, swelling tests were carried out. As a result, the unconfined compressive strength of samples with 10% Microsilica and 3% Lime in curing time of 28 days was obtained about 50 times larger than the strength of the untreated samples. On the other hand, the samples stabilized only with 1% Lime showed considerable swelling potential while adding only 1% Microsilica caused a considerable reduce in the amount of swelling. Unconfined compressive strength of samples containing 1% Microsilica and 1% Lime was about 12 times larger than the strength of the untreated samples and these samples showed less swelling potential. Then, these amounts are considered as the optimal amounts, which are used in the road construction projects. Also, the results obtained from scanning the samples using electron microscope illustrated that the Microsilica causes to form crystalline micro-structures in the soil which is the main cause for increasing the strength of stabilized samples