Novel Approach to Integration of Numerical Modeling and Field Observations for Deep Excavations

Hashash, Youssef M. A.; Marulanda, Camilo; Ghaboussi, Jamshid; Jung, Sungmoon

doi:10.1061/(asce)1090-0241(2006)132:8(1019)

Cited by 93 publications

(40 citation statements)

References 32 publications

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“…In the area of computational mechanics, an informational method using neural networks was first proposed by Ghaboussi et al(1990;1991) Shin et al(2000) proposed a robust framework for the training of material constitutive models with an interactive correction method of stress and strain. Self-learning simulation has been applied to demonstrate the feasibility of extracting geo-material constitutive behavior from site measurement (Hashash et al, 2003;2006).…”

Section: Biologically Inspired Modeling: Informational Modelingmentioning

confidence: 99%

Information-Based Hybrid Modeling Framework on the Systematic use of Artificial Neural-Networks

Kim¹,

Ghaboussi²

2012

Journal of the Computational Structural Engineering Institute o

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In this study, a new information-based hybrid modeling framework is proposed. In the hybrid framework, a conventional mathematical model is complemented by the informational methods. The basic premise of the proposed hybrid methodology is that not all features of system response are amenable to mathematical modeling, hence considering informational alternatives. This may be because (i) the underlying theory is not available or not sufficiently developed, or (ii) the existing theory is too complex and therefore not suitable for modeling within building frame analysis. The role of informational methods is to model aspects that the mathematical model leaves out. Autoprogressive algorithm and self-learning simulation extract the missing aspects from a system response. In a hybrid framework, experimental data is an integral part of modeling, rather than being used strictly for validation processes. The potential of the hybrid methodology is illustrated through modeling complex hysteretic behavior of beam-to-column connections.

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Section: Biologically Inspired Modeling: Informational Modelingmentioning

confidence: 99%

Information-Based Hybrid Modeling Framework on the Systematic use of Artificial Neural-Networks

Kim¹,

Ghaboussi²

2012

Journal of the Computational Structural Engineering Institute o

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“…16) of 2-D quay wall-soil systems [110]. The concept of employing experimental data sets and local in-situ measurements to calibrate complex constitutive laws with large number of parameters have been systematically investigated by Yang and Elgamal [100], Levasseur et al [56,57], Hashash et al [46,35], and Calvello and Finno: [15].…”

Section: Local Identificationmentioning

confidence: 99%

A survey of geotechnical system identification techniques

Oskay

Zeghal

2011

Soil Dynamics and Earthquake Engineering

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Identification and inverse problem techniques play an important role in the characterization and modeling of geotechnical systems. These techniques have been used in estimation of system parameters, model development and calibration, as well as simulation of earthquake ground motions. The recent availability of high quality seismic records of sites equipped with downhole accelerometer arrays led to a burgeoning of identification and inverse problem studies involving geotechnical systems. This paper presents a survey of system identification techniques and analyses of full-and small-scale soil systems, with an emphasis on geotechnical earthquake engineering problems.

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“…In this paper, the SelfSim learning inverse analysis approach introduced by Hashash et al [29] for 2D excavation analyses is extended to learn excavation response in the 3D analysis. The extended 3D SelfSim framework is used to learn Ford Motor Design Center excavation measured responses and extract underlying soil behavior.…”

Section: Y M a Hashash H Song And A Osoulimentioning

confidence: 99%

“…The extracted stress-strain pairs from two complementary numerical analyses are used to train the NN material model(s) [29,43]. SelfSim has been verified for extracting soil constitutive behavior in 2D from clayey excavation sites [29], sandy soil of a full scale model [44], and instrumentation studies [45,46]. The use of 3D meshes in SelfSim to study excavation problems has the appeal that some of the modeling approximations in 2D plain strain assumption would be eliminated.…”

Section: Self Learning Simulations (Selfsim)mentioning

confidence: 99%

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Three‐dimensional inverse analyses of a deep excavation in Chicago clays

Hashash

Song

Osouli

2010

Num Anal Meth Geomechanics

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SUMMARYNumerical models are commonly used to estimate excavation-induced ground movements. Twodimensional (2D) plain strain assumption is typically used for the simulation of deep excavations which might not be suitable for excavations where three-dimensional (3D) effects dominate the ground response. This paper adapts an inverse analysis algorithm to learn soil behavior from field measurements using a 3D model representation of an excavation. The paper describes numerical issues related to this development including the generation of the 3D model mesh from laser scan images of the excavation. The inverse analysis to extract the soil behavior in 3D is presented. The model captures the measured wall deflections. Although settlements were not sufficiently measured, the predicted settlements around the excavation site reflected strong 3D effects and were consistent with empirical correlations.

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Novel Approach to Integration of Numerical Modeling and Field Observations for Deep Excavations

Cited by 93 publications

References 32 publications

Information-Based Hybrid Modeling Framework on the Systematic use of Artificial Neural-Networks

Information-Based Hybrid Modeling Framework on the Systematic use of Artificial Neural-Networks

A survey of geotechnical system identification techniques

Three‐dimensional inverse analyses of a deep excavation in Chicago clays

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