Application of geotechnical monitoring in tunnels with neural networks and finite elements methods

Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) “Σχεδιασμός και Κατασκευή Υπόγειων Έργων

Mathematical Problems in Rock Mechanics and Rock Engineering

With increasing requirements for energy, resources and space, rock engineering projects are being constructed more often and are operated in large-scale environments with complex geology. Meanwhile, rock failures and rock instabilities occur more frequently, and severely threaten the safety and stability of rock engineering projects. It is well-recognized that rock has multi-scale structures and involves multi-scale fracture processes. Meanwhile, rocks are commonly subjected simultaneously to complex static stress and strong dynamic disturbance, providing a hotbed for the occurrence of rock failures. In addition, there are many multi-physics coupling processes in a rock mass. It is still difficult to understand these rock mechanics and characterize rock behavior during complex stress conditions, multi-physics processes, and multi-scale changes. Therefore, our understanding of rock mechanics and the prevention and control of failure and instability in rock engineering needs to be furthered. The primary aim of this Special Issue “Mathematical Problems in Rock Mechanics and Rock Engineering” is to bring together original research discussing innovative efforts regarding in situ observations, laboratory experiments and theoretical, numerical, and big-data-based methods to overcome the mathematical problems related to rock mechanics and rock engineering. It includes 12 manuscripts that illustrate the valuable efforts for addressing mathematical problems in rock mechanics and rock engineering

Unplanned dilution and ore-loss optimisation in underground mines via cooperative neuro-fuzzy network

The aim of study is to establish a proper unplanned dilution and ore-loss (UB: uneven break) management system. To achieve the goal, UB prediction and consultation systems were established using artificial neural network (ANN) and fuzzy expert system (FES). Attempts have been made to illuminate the UB mechanism by scrutinising the contributions of potential UB influence factors. Ultimately, the proposed UB prediction and consultation systems were unified as a cooperative neuro fuzzy system

Theory and Practice of Tunnel Engineering

Tunnel construction is expensive when compared to the construction of other engineering structures. As such, there is always the need to develop more sophisticated and effective methods of construction. There are many long and large tunnels with various purposes in the world, especially for highways, railways, water conveyance, and energy production. Tunnels can be designed effectively by means of two and three-dimensional numerical models. Ground–structure interaction is one of the significant factors acting on economic and safe design. This book presents recent data on tunnel engineering to improve the theory and practice of the construction of underground structures. It provides an overview of tunneling technology and includes chapters that address analytical and numerical methods for rock load estimation and design support systems and advances in measurement systems for underground structures. The book discusses the empirical, analytical, and numerical methods of tunneling practice worldwide

Topics of Analytical and Computational Methods in Tunnel Engineering

In this chapter, a selection of tunneling topics is presented, following the evolution of methods and tools from analytical to computational era. After an introductory discussion of the importance of elasticity and plasticity in tunneling, some practical topics are presented as paradigms to show the successful application of them in achieving a solution. The circular and horseshoe tunnel sections served as the basis of the elastic analysis of deep tunnels. Practical aspects such as influence zone and elastic convergences in both cases are examined. In the case of circular tunnels, the estimation of plastic zone formation is discussed for a selection of strength criteria. After a detailed discussion of the influence of surface proximity, the elastic and plastic analysis of shallow tunnels is examined in some detail. The presentation is completed by a short presentation of computational methods. An overview of recent developments and a classification of the methods are presented, and then some problems for the case of anisotropic rocks have been presented using finite element method (FEM). The last topic is the application of artificial intelligence (AI) tools in interpreting data and in estimating the relative importance of parameters involved in the problem of tunneling-induced surface settlements. In the conclusions a short discussion of the main topics presented follows

Analysis of basic motor behaviors in quadrupeds

Ability to perform locomotion in different directions and maintain upright body posture is crucial for normal life. At present, mice, which allows employing genetic approaches, are widely used in studying the locomotor system. In these investigations different experimental setups are used to evoke locomotion. First aim of the present study was to compare kinematics of forward (FW) and backward (BW) locomotion performed in different environmental conditions (i.e. in a tunnel, on a treadmill and on an air-ball). On all set-ups, average speed, step amplitude and swing duration during BW locomotion were significantly smaller compared to those observed during FW locomotion. The extent of rostro-caudal paw trajectory in relation to the hip projection to the surface (HP) strongly depended on hip height. With high hip height, the trajectory was symmetrical in relation to HP (middle steps). When hip was low, steps were either displaced rostrally (anterior steps) or caudally (posterior steps) in relation to HP. During FW locomotion, predominantly anterior and posterior steps were observed, respectively, on the treadmill and air-ball, while all three stepping forms were observed in the tunnel. We observed only anterior steps during BW locomotion. Intralimb coordination depended on the form of stepping. Second aim of the present study was to reveal the role of two populations of commissural interneurons (V0V and V0D CINs) in control of a number of basic motor behaviours (BW locomotion, scratching, righting, and postural corrections). For this purpose two types of knockout mice (Vglut2Cre;Dbx1DTA mice and Hoxb8Cre;Dbx1DTA mice with only V0V and all V0 CINs ablated, respectively) as well as wild-type littermates were used. Our results suggest that the functional effect of excitatory V0V CINs during BW locomotion and scratching is inhibitory, and that execution of scratching involves active inhibition of the contralateral scratching CPG mediated by V0V CINs. By contrast, V0D CINs are elements of spinal postural network, generating postural corrections. Finally, both V0D and V0V CINs contribute to generation of righting behavior. Thus, our study shows the differential contribution of V0 neuron subpopulations in generation of diverse motor acts. Single steps in different directions are used for control of balance or body configuration. However, our knowledge about neural mechanisms responsible for their generation is limited. The third aim of the present study was to characterize postural response to disturbance of basic body configuration caused by forward, backward or outward displacement of the hindlimb. In intact rabbits, displacement of the hindlimb in any direction caused a postural response consisting of two components. First, a lateral trunk movement towards the supporting (contralateral) hindlimb was performed, and then a corrective step in the direction opposite to the direction of the initial limb displacement was executed. These two components were generated by different mechanisms activated in a strict order by sensory information from the deviated limb signalling distortion of the limb/limb-trunk configuration. We have shown that the integrity of the forebrain was not critical for generation of this postural response. We proposed a hypothesis about operation of mechanisms generating the postural response characterized in the present study

An Experimental-Intelligent Method to Predict Noise Value of Drilling in Dimension Stone Industry

The noise of drilling in the dimension stone business is unbearable for both the workplace and the people who work there. In order to reduce the negative effects drilling has on the health of the environment, the drilling noise has to be measured, assessed, and controlled. The main purpose of this work is to investigate an experimental-intelligent method to predict the noise value of drilling in the dimension stone industry. For this purpose, 135 laboratory tests are designed on five types of rocks (four types of hard rock and one type of soft rock): and their results are measured in the first step. In the second step, due to the unpredicted and uncertain issues in this case, artificial intelligence (AI) approaches are applied, and the modeling is conducted using three intelligent systems (IS): namely an adaptive neuro-fuzzy inference system-SCM (ANFIS-SCM): an adaptive neuro-fuzzy inference system-FCM (ANFIS-FCM): and the radial basis function network (RBF) neural network. 75% of the samples are considered for training, and the rest for testing. Several models are constructed, and the results indicate that although there is no significant difference between the models according to the performance indices, the proposed construction of ANFIS-SCM can be considered as an efficient tool in the evaluation of drilling noise. Finally, several scenarios are designed with different input modes, and the results obtained prove that the types of rock and the drill bits are more important than the operational characteristics of the machine

Spatial Interpolation of SPT with Artificial Neural Network

In large infrastructure projects, initial geotechnical investigation is conducted at large spacing (~ 100m to 250m), in which SPT is the common test performed while dynamic tests are limited in number. The preliminary planning and design of the buildings are performed based on this information. Hence, estimate of dynamic properties of soil (say, shear wave velocity) at building locations becomes necessary. This can be performed by estimation of SPT at building locations, by interpolation from borehole locations, and thereafter using correlation expressions for estimating shear wave velocity at building location. Interpolation of SPT has been handled earlier in literature with statistical and geospatial techniques. In this article, an artificial intelligence technique, namely, artificial neural network (ANN) is explored for addressing this problem. ANN allows multiple degrees of freedom to data and optimizes weights and biases of the network to yield the best possible estimates of the desired output, in this case, the SPT at intermediate locations. ANN is known to be robust in handling data with noise and thus would be suitable for this application. Five neighbouring points were found suitable for efficient and accurate spatial interpolation of SPT using ANN with two to three neurons in one hidden layer. The performance was very good (correlation higher than 0.9 and errors lower than 2) and better than the geo-statistical approaches reported in literature (correlation lower than 0.9 and errors higher than 6). Within the limits of the study, the number of degrees of freedom (varying from 9 to 37) of the ANN did not affect its generalization capability