Tunneling-induced ground movement and building damage prediction using hybrid artificial neural networks

The construction of tunnels in urban areas may cause ground displacement which distort and damage overlying buildings and services. Hence, it is a major concern to estimate tunneling-induced ground movements as well as to assess the building damage. Artificial neural networks (ANN), as flexible non-linear function approximations, have been widely used to analyze tunneling-induced ground movements. However, these methods are still subjected to some limitations that could decrease the accuracy and their applicability. The aim of this research is to develop hybrid particle swarm optimization (PSO) algorithm-based ANN to predict tunneling-induced ground movements and building damage. For that reason, an extensive database consisting of measured settlements from 123 settlement markers, geotechnical parameters, tunneling parameters and properties of 42 damaged buildings were collected from Karaj Urban Railway project in Iran. Based on observed data, the relationship between influential parameters on ground movements and maximum surface settlements were determined. A MATLAB code was prepared to implement hybrid PSO-based ANN models. Finally, an optimized hybrid PSO-based ANN model consisting of eight inputs, one hidden layer with 13 nodes and three outputs was developed to predict three-dimensional ground movements induced by tunneling. In order to assess the ability and accuracy of the proposed model, the predicted ground movements using proposed model were compared with the measured settlements. For a particular point, ground movements were obtained using finite element model by means of ABAQUS and the results were compared with proposed model. In addition, an optimized model consisting of seven inputs, one hidden layer with 21 nodes and one output was developed to predict building damage induced by ground movements due to tunneling. Finally, data from damaged buildings were used to assess the ability of the proposed model to predict the damage. As a conclusion, it can be suggested that the newly proposed PSO-based ANN models are able to predict three-dimensional tunneling-induced ground movements as well as building damage in tunneling projects with high degree of accuracy. These models eliminate the limitations of the current ground movement and building damage predicting methods

Interaction of segmental tunnel linings and dip-slip faults—tabriz subway tunnels

In some subsurface urban development projects, bedrock faults intersecting with the tunnel path are inevitable. Due to the high costs of urban tunnel projects, it is necessary to study the behavior of such concrete structures under fault movement risks. Using an advanced 3D numerical finite difference code and a plastic hardening constitutive model for the soil, this paper examined the performance of the straight and oblique segmented structures of Tabriz Subway Line 2 under large deformations. The Tabriz Line 2 tunnel passes through a reverse fault called the Baghmisheh Fault. The fault–tunnel simulations were validated by centrifuge tests on the segmental tunnel for normal faulting. In the centrifuge tests and validation models, there was a maximum difference of 15%. According to the results of the Tabriz Line 2 tunnel under reverse faulting, segmental structures outperform no-joint linings when it comes to fault movement. During reverse fault movement, line 2 segments did not collapse but showed slight deformations. However, continuous structures collapsed under faulting, i.e., the structural forces created exceeded the section strength capacity. Among the segmental structures, the lining with oblique joints showed better behavior against faulting than the lining with straight joints. For better tunnel performance under fault movement, oblique joints should be used in segmental structures in faulting areas.(undefined

A Novel Approach for Blast-Induced Flyrock Prediction Based on Imperialist Competitive Algorithm and Artificial Neural Network

Flyrock is one of the major disturbances induced by blasting which may cause severe damage to nearby structures. This phenomenon has to be precisely predicted and subsequently controlled through the changing in the blast design to minimize potential risk of blasting. The scope of this study is to predict flyrock induced by blasting through a novel approach based on the combination of imperialist competitive algorithm (ICA) and artificial neural network (ANN). For this purpose, the parameters of 113 blasting operations were accurately recorded and flyrock distances were measured for each operation. By applying the sensitivity analysis, maximum charge per delay and powder factor were determined as the most influential parameters on flyrock. In the light of this analysis, two new empirical predictors were developed to predict flyrock distance. For a comparison purpose, a predeveloped backpropagation (BP) ANN was developed and the results were compared with those of the proposed ICA-ANN model and empirical predictors. The results clearly showed the superiority of the proposed ICA-ANN model in comparison with the proposed BP-ANN model and empirical approaches

A gene expression programming model for predicting tunnel convergence

Underground spaces have become increasingly important in recent decades in metropolises. In this regard, the demand for the use of underground spaces and, consequently, the excavation of these spaces has increased significantly. Excavation of an underground space is accompanied by risks and many uncertainties. Tunnel convergence, as the tendency for reduction of the excavated area due to change in the initial stresses, is frequently observed, in order to monitor the safety of construction and to evaluate the design and performance of the tunnel. This paper presents a model/equation obtained by a gene expression programming (GEP) algorithm, aiming to predict convergence of tunnels excavated in accordance to the New Austrian Tunneling Method (NATM). To obtain this goal, a database was prepared based on experimental datasets, consisting of six input and one output parameter. Namely, tunnel depth, cohesion, frictional angle, unit weight, Poisson's ratio, and elasticity modulus were considered as model inputs, while the cumulative convergence was utilized as the model's output. Configurations of the GEP model were determined through the trial-error technique and finally an optimum model is developed and presented. In addition, an equation has been extracted from the proposed GEP model. The comparison of the GEP-derived results with the experimental findings, which are in very good agreement, demonstrates the ability of GEP modeling to estimate the tunnel convergence in a reliable, robust, and practical manner

Ground Behaviour Around a Tunnel Using Various Soil Models

Finite Element (FE) analyses are used world widely in geotechnical engineering to obtain the soil displacement caused by tunnelling. The surface settlement induced by tunnelling predicted by FE is known to be wider and shallower than the field measurements particularly for stiff clays with high coefficient of earth pressure at rest, K0. It has been recognized that neglecting the non-linearity, anisotropy and threedimensional effects of the soil model as well as K0 condition can be the reasons of this discrepancy. Unfortunately, such numerical studies were only limited to the problem in the plane strain condition whereas tunnelling is obviously a three dimensional (3D) problem. This paper compares 3D FE modelling of tunnel constructions in stiff soil of London Clay using non-linear soil model with low and high K0 regimes. It was found that modelling using isotropic non-linear soil with low value of K0 gave the best matched-fit data on the observed greenfield surface settlement as opposed to the other soil models. In addition, the model is able to replicate the steady state condition of ground movement after the completion of tunnel construction that is when the tunnel face has passed seven times of the tunnel diameter beyond the boundary point. This steady-state condition is not possible to simulate using other soil models

Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks

There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease-19 (COVID-19). We aimed to a) identify complement-related genetic variants associated with the clinical outcomes of ICU hospitalization and death, b) develop an artificial neural network (ANN) predicting these outcomes and c) validate whether complement-related variants are associated with an impaired complement phenotype. We prospectively recruited consecutive adult patients of Caucasian origin, hospitalized due to COVID-19. Through targeted next-generation sequencing, we identified variants in complement factor H/CFH, CFB, CFH-related, CFD, CD55, C3, C5, CFI, CD46, thrombomodulin/THBD, and A Disintegrin and Metalloproteinase with Thrombospondin motifs (ADAMTS13). Among 381 variants in 133 patients, we identified 5 critical variants associated with severe COVID-19: rs2547438 (C3), rs2250656 (C3), rs1042580 (THBD), rs800292 (CFH) and rs414628 (CFHR1). Using age, gender and presence or absence of each variant, we developed an ANN predicting morbidity and mortality in 89.47% of the examined population. Furthermore, THBD and C3a levels were significantly increased in severe COVID-19 patients and those harbouring relevant variants. Thus, we reveal for the first time an ANN accurately predicting ICU hospitalization and death in COVID-19 patients, based on genetic variants in complement genes, age and gender. Importantly, we confirm that genetic dysregulation is associated with impaired complement phenotype.- Pfizer Pharmaceuticals(undefined

Zonation of landslide hazards based on weights of evidence modeling along Tehran-Chalos Road Path, Iran

In this study, GIS and remote sensing (RS) technology were applied to investigate zonation of the landslide hazards of the Tehran-Chalos road path in the north of Iran. Several affecting parameters of the occurrence of landslides were analyzed. The factors contributing to the hazard in an area can usually be identified, results of the investigations frequently being presented as a landslide hazard zonation (Lhz) map which reveals zones of similar risk of landslide occurrence. Tehran-Chalos road path is one of the susceptible areas to landslides in Iran. In this particular area, several landslides were occured. Landslides caused damage or disturbance to villages, farmlands and road, intensification of the superficial erosion and as a result an increment in rate of transportation of sediments. The method of landslide zonation used in this study was established upon simple grid unit. The causative factors include lithology, hydrology, elevation, drain distance to river, tectonic and seismotectonic, slope angle, human activities and distribution of plant. They are derived from geological map, Spot imagery data and Digital Elevation Model based on RS and GIS technology. For each grid unit, the incidence of land sliding and an assessment of the contributory factors were recorded in terms of a surface percentage index. A computer program was written to calculate the Lhz for each unit. It was also used to prepare the Lhz map. The study of the area has been classified into five categories of relative landslide hazard, namely, very low, low, moderate, high and very high. As a result, it can be concluded that 7% of this particular area has a high or very high landslide hazard

Effects of tunnel depth and relative density of sand on surface settlement induced by tunneling

Tunnelling in densely populated areas is generally associated with undesirable ground movement and subsequent damage to adjacent buildings. Many parameters are contributed to the ground movements during tunnelling in which non-linear relationships are established between these parameters and ground movements. This paper presents the effects of tunnel depth and relative density of sand on surface settlement induced by tunneling by means of parametric study through finite element modelling. In this regard, tunnel excavation in sand with two different relative densities of 30% and 75% was investigated. In addition, effects of tunneling in different cover to diameter ratio of 1, 2, 3, and 4 were analysed. The results show that increasing in the value of the relative density of sand reduces the ground movements induced by tunneling. In addition, shallow tunneling in loose sand produces remarkable movements around the tunnel and on the ground surface

Bearing capacity of shallow foundation's prediction through hybrid artificial neural networks

The utilization of Artificial Neural Network (ANN) for bearing capacity estimation has some disadvantages such as getting trapped in local minima and slow rate of learning. Recent developments of optimization algorithms such as Particle Swarm Optimization (PSO) have made it possible to overcome ANN drawbacks and improve its efficiency. This paper presents a unified approach of ANN based on PSO algorithm to predict bearing capacity of shallow foundations in granular soils. To generate the network, numbers of 40 datasets including the recorded cases of fullscale axial compression load test on shallow foundations in granular soils were collected from literatures. Each dataset refers to a set of 6 inputs consisted of footing length and width, embedded depth of the footing, average vertical effective stress of the soil, friction angle of the soil, and ground water level as well as one output consisted of the ultimate axial bearing capacity. Several sensitivity analyses were conducted to determine the optimum parameters of PSO algorithm and the network architecture was determined following the trial and error method. The results demonstrate that the presented model predicts the bearing capacity of shallow foundations with high degree of accuracy

Investigation on the effects of twin tunnel excavations beneath a road underpass

Excavation of tunnels underneath cities often intrudes the existence of piled foundation and in severe cases, can cause damage to the overlying structures. As there are very limited published case studies concerning understanding of the interaction between piled structure and tunneling, there is a significant uncertainty regarding tunnel-pile interaction. In this paper, a case study of the effects of two subway tunnels on the contiguous pile walls which support a road underpass is investigated using three-dimensional Finite Element simulations. The interaction between the tunnels and piles is investigated with a special attention to the effect of tunnel face pressures. Through the numerical modelling and field data, it is shown with presence of the piles, the minimum pressure to support the tunnel face is less than minimum face pressure in the green field condition. Field experience indicates that excessive tunnel face pressure can cause temporary heave to the ground surface but also cause damage to the cutter head of tunnel boring machine