Neural network based prediction of ground surface settlements due to tunnelling

Kim, C.Y.; Bae, Gyu-Jin; Hong, Soojung; Park, Chi Hyun; Moon, Hyun-Koo; Shin, Hyu-Soung

doi:10.1016/s0266-352x(01)00011-8

Cited by 206 publications

(67 citation statements)

References 14 publications

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“…In this paper, an approach using a first-order method based upon the relative strength effect (RSE) to evaluate the relative importance of input variables to the prediction of viral presence is used. This approach is based upon the work of Kim et al (13), who proposed the RSE as a means to differentiate the relative influence of different input variables. They defined the RSE as the partial derivative of the output variable y k , ‫ץ‬y k ‫ץ‬x i .…”

mentioning

confidence: 99%

Artificial Neural Network Prediction of Viruses in Shellfish

Viswanathan

Neelakantan

Lingireddy

et al. 2005

Appl Environ Microbiol

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A database was probed with artificial neural network (ANN) and multivariate logistic regression (MLR) models to investigate the efficacy of predicting PCR-identified human adenovirus (ADV), Norwalk-like virus (NLV), and enterovirus (EV) presence or absence in shellfish harvested from diverse countries in Europe (Spain, Sweden, Greece, and the United Kingdom). The relative importance of numerical and heuristic input variables to the ANN model for each country and for the combined data was analyzed with a newly defined relative strength effect, which illuminated the importance of bacteriophages as potential viral indicators. The results of this analysis showed that ANN models predicted all types of viral presence and absence in shellfish with better precision than MLR models for a multicountry database. For overall presence/absence classification accuracy, ANN modeling had a performance rate of 95.9%, 98.9%, and 95.7% versus 60.5%, 75.0%, and 64.6% for the MLR for ADV, NLV, and EV, respectively. The selectivity (prediction of viral negatives) was greater than the sensitivity (prediction of viral positives) for both models and with all virus types, with the ANN model performing with greater sensitivity than the MLR. ANN models were able to illuminate site-specific relationships between microbial indicators chosen as model inputs and human virus presence. A validation study on ADV demonstrated that the MLR and ANN models differed in sensitivity and selectivity, with the ANN model correctly identifying ADV presence with greater precision.Health risks associated with the consumption of virally contaminated shellfish are well documented, as is the need for a more reliable viral indicator system by the industry (14, 17). Interdisciplinary studies are needed to define the underlying relationships between harvest area water quality, shellfish type, treatment processes, and viral presence, particularly with the advent of advanced detection and modeling methods. New understandings can be obtained with the application of new data-driven, fuzzy-logic-based models that can handle multiple, interrelated inputs and learn complex relationships. However, for the application of these new models, large, robust, multivariable, complete, and well-controlled datasets need to be created. A multicountry study in Europe has collected vital data in an effort to relate the viral contamination of shellfish with potential indicators. Analysis of these results has been reported earlier by 7), and the database consisted of 468 individual observations from geographically diverse areas collected over 18 months. The resultant database was provided to a team of engineers and modeling experts for further probing with new artificial neural network (ANN) modeling tools under the hypothesis that these new modeling tools would be able to better define the relationships between viral presence/absence and potential water quality indicators than multivariate logistic regression (MLR) and provide more precise predictions with ANN models.Neural network model...

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mentioning

confidence: 99%

Artificial Neural Network Prediction of Viruses in Shellfish

Viswanathan

Neelakantan

Lingireddy

et al. 2005

Appl Environ Microbiol

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“…The settlement of the ground surface (Kim et al 2001;Sou-Sen and Hsien-Chuang 2004;Suwansawat and Einstein 2006) around foundation excavation is explained by Figs. 8 and 9.…”

Section: Settlement Of Ground Surface Around Foundation Excavationmentioning

confidence: 99%

Study on Deformation Characteristics of Deep Foundation Excavation in Soft-Soil and the Response of Different Retaining Configurations

Liu

2011

Geotech Geol Eng

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In recent years, many researchers have considered the mechanical characteristics of deep foundation excavation in soft-soil. The analysis of these deep excavations requires consideration of an uncertain, nonlinear, dynamic and complicated system, and involves consideration of soil strength, stability, deformation, fluid flow and interaction of soil and different retaining configurations. It is difficult to describe such a nonlinear system using traditional analysis. Therefore, in order to accurately describe the mechanical behavior of a representative deep excavation of the subway station, in this case, 3-Dimensional geotechnical numerical analysis method using FLAC3D software was applied. Using this tool, a study considering earth pressure, soil deformation and settlement was carried out. Furthermore, the response of different retaining configurations was deeply investigated. Triaxial cement mixing piles were considered as a way to optimize deformation of the deep excavation and reduce settlement of the ground surface and the railway embankment. The analysis indicated that the deeper the foundation excavation was, the larger the surface settlement and the smaller the earth pressure. The analysis also considered the mechanical effect of varying the wall thickness and the wall depth on the structure's deformation characteristics. The simulations indicated that a wall thickness of less than 1.4 m effectively reduced wall horizontal displacement, ground surface settlement and uneven settlement of railway embankment. While a variable wall embedded depth that was less than 52 m also changed the settlement of the excavation deformation and the ground surface. Therefore, the numerical results can agree with the practical project to imply that numerical method in the paper is applicable and reliable, which provides a new thought to research on deep excavation in soft-soil.

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“…Then, the neural network method was used in many research subjects in engineering geology and civil engineering. The neural network has been applied in structural damage inspection [8], soft rocks strength prediction [9], ground vibration prediction [10], ground surface settlement [11,12], engineering cost prediction [13,14], concrete expansion prediction [15], soil swell potential prediction [16], triaxial compression behavior prediction [17], saturated hydraulic conductivity prediction [18], and other engineering subjects [19][20][21]. Only a fraction of the studies were about predicting the frostheaving behavior of saline soil in Western Jilin [22].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Frost‐Heaving Behavior of Saline Soil in Western Jilin Province, China, by Neural Network Methods

Zhang

Wang

Huo

et al. 2017

Mathematical Problems in Engineering

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In this study, backpropagation neural network (BPNN) and generalized regression neural network (GRNN) approaches are used to predict the frost-heaving ratio (FR) of the saline soil specimen collected from Nong'an, Western Jilin, China. Four variables, namely, water content (WC), compactness, temperature, and content of soluble salts (CSS), are considered in predicting FR. A total of 360 pieces of data, collected from the experimental results, in which 30 pieces of data were selected randomly as the testing set data and the rest of the data were treated as the training set data, are applied to develop the prediction models. The predicted data from the models are compared with the experimental data. Then, the results of the two approaches are compared to obtain a relatively reliable model. Results indicate that the prediction model for the FR of saline soil in Nong'an can be successfully established using the GRNN method. Four new GRNN models are constructed for sensitivity analysis to assess the influence degree of the influencing factors, and the results indicate that water content is the most influential variable in the FR of the saline soil specimen, whereas content of soluble salts is the least influential variable.

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Neural network based prediction of ground surface settlements due to tunnelling

Cited by 206 publications

References 14 publications

Artificial Neural Network Prediction of Viruses in Shellfish

Artificial Neural Network Prediction of Viruses in Shellfish

Study on Deformation Characteristics of Deep Foundation Excavation in Soft-Soil and the Response of Different Retaining Configurations

Prediction of Frost‐Heaving Behavior of Saline Soil in Western Jilin Province, China, by Neural Network Methods

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