CPT-Based Nonlinear Stress–Strain Approach for Evaluating Foundation Settlement: Analytical and Numerical Analysis

Valikhah, Fatemeh; Eslami, Abolfazl

doi:10.1007/s13369-019-04034-y

Cited by 7 publications

(6 citation statements)

References 26 publications

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“…Backpropagation involves adjusting the weight and threshold in the direction from output to input, allowing the system to learn and adjust based on the errors identified. This process is iterative, with the network continually re-evaluating and refining its judgments based on new data [23]. Error backpropagation is the process of transmitting output error back through the hidden layer to the input layer.…”

Section: B Calculation Of Related Parametersmentioning

confidence: 99%

Research on Settlement Prediction of Building Foundation in Smart City Based on BP Network

Wei¹

2023

IJACSA

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In the construction process of high-rise buildings, it is necessary to predict the settlement and deformation of the foundation, and the current prediction methods are mainly based on empirical theoretical calculations and methods and more accurate numerical analysis methods. In the face of the interference of complex and ever-changing terrain and parameter values on prediction methods, in order to accurately determine the settlement of building foundations, this study designed a smart city building foundation settlement prediction method based on BP neural network. Firstly, a real-time dynamic monitoring unit for building foundation settlement was constructed using Wireless Sensor Network (WSN) technology. Then, the monitoring data was used to calculate the relevant parameters of building foundation settlement through layer sum method. Finally, input the monitoring data into the BP network results, adjust the weights of the output layer and hidden layer using settlement related parameters, and output the settlement prediction results of the smart city building foundation through training. The study selected average error and prediction time as evaluation criteria to test the feasibility of the method proposed in this article. This method can effectively predict foundation settlement, with an average prediction error always less than 4% and a prediction process time always less than 49ms.

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Section: B Calculation Of Related Parametersmentioning

confidence: 99%

Research on Settlement Prediction of Building Foundation in Smart City Based on BP Network

Wei¹

2023

IJACSA

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“…[44] updated the values of his previously proposed modulus number, m, for normally consolidated silt and sand as a function of porosity, n, which in this paper has been converted to the more widely used void ratio, e. Figure 7 shows the range of modulus numbers for normally consolidated silts sands as a function of void ratio for different soil densities. Methods have been proposed in the literature where the modulus num lated directly to the cone resistance [46] without considering the effect of dep stress) as discussed above.…”

Section: Modulus Number-virgin Loadingmentioning

confidence: 99%

Determination of Constrained Modulus of Granular Soil from In Situ Tests—Part 1 Analyses

Massarsch

2023

Geotechnics

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Assessing the constrained modulus is a critical step in calculating settlements in granular soils. This paper describes a novel concept of how the constrained modulus can be derived from seismic tests. The advantages and limitations of seismic laboratory and field tests are addressed. Based on a comprehensive review of laboratory resonant column and torsional shear tests, the most important parameters affecting the shear modulus, such as shear strain and confining stress, are defined quantitatively. Also, Poisson’s ratio, which is needed to convert shear modulus to constrained modulus, is strain-dependent. An empirical relationship is presented from which the variation in the secant shear modulus with shear strain can be defined numerically within a broad strain range (10−4–10−0.5%). The tangent shear modulus was obtained by differentiating the secant shear modulus. According to the tangent modulus concept, the tangent constrained modulus is governed by the modulus number, m, and the stress exponent, j. Laboratory test results on granular soils are reviewed, based on which it is possible to estimate the modulus number during virgin loading and unloading/reloading. A correlation is proposed between the small-strain shear modulus, G0, and the modulus number, m. The modulus number can also be derived from static cone penetration tests, provided that the cone resistance is adjusted with respect to the mean effective stress. In a companion paper, the concepts presented in this paper are applied to data from an experimental site, where different types of seismic tests and cone penetration tests were performed.

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“…Furthermore, other parameters like the footing width (B) and embedment depth (Df) of the PLTs were documented. For CPT data, the depth of 2B has been regarded as the effective zone of load influence, according to the majority of the common methods for the estimation of the settlement beneath the foundation under symmetric loading (Valikhah and Eslami 2019). The parameters acquired from CPT and used in the analyses are the tip resistance qt (qc corrected for pore pressure) and skin friction 198 ( = ).…”

Section: Review Of the Databasementioning

confidence: 99%

A Framework to Predict the Load-Settlement Behavior of Shallow Foundations in a Range of Soils from Silty Clays to Sands using CPT Records

MolaAbasi

Khajeh

Chenari

et al. 2021

Preprint

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Using a set of cone penetration test (CPT) records,the current paper develops a general framework based on regression analyses to model the load-settlement (q-s) behavior of shallow foundations resting on a variety of soils ranging from silty clays to sands.A three-parameter hyperbolic function is employed to rigorously examine the obtainedq-s curves, and to determine the model parameters.Also, the results of someCPT soundings, including the corrected cone tip resistance (qt) and the skin friction (Rf),are adopted to predict the results of plate load tests(PLT). The findingscorroborate the high accuracy of the proposed model, thereasonable performance of the hyperbolic function and the use of the Volterra series to predict the q-s curves.Moreover, the obtained curves from the newly developed model arecompared to those from other methods in the literature which cross-confirms the efficacyof the current model. Asensitivity analysis isalso conductedand the exclusive effects of all the contributing parameters are assessed among which Rfis shown to be the most influential. Ultimately, simple solutionsare adoptedto determine variouskey geotechnical parameters, like the ultimate bearing capacity (qult), the allowable bearing capacity (qa) andthe modulus of subgrade reaction (ks).

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CPT-Based Nonlinear Stress–Strain Approach for Evaluating Foundation Settlement: Analytical and Numerical Analysis

Cited by 7 publications

References 26 publications

Research on Settlement Prediction of Building Foundation in Smart City Based on BP Network

Research on Settlement Prediction of Building Foundation in Smart City Based on BP Network

Determination of Constrained Modulus of Granular Soil from In Situ Tests—Part 1 Analyses

A Framework to Predict the Load-Settlement Behavior of Shallow Foundations in a Range of Soils from Silty Clays to Sands using CPT Records

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