Determination of Multilayer Soil Strength Parameters Using Genetic Algorithm

Hashemi, Seyyed Mohammad Reza; Rahmani, Iraj

doi:10.28991/cej-03091167

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…Despite limitations related to accuracy of solutions obtained with RM, this method is widely used in practice due to its simplicity and fast computation time (Brownjohn et al, 2001(Brownjohn et al, , 2003Rechea et al, 2008;Zhang et al, 2013;Chen et al, 2014;Mosavi et al, 2014;Feng and Feng 2015;Sanayei et al, 2015;Hashemi and Rahmani 2018). This underscores the needs for ease of use, as well as accuracy, to ensure practical adoption of more modern data-interpretation methodologies to support the extrapolations that are needed for good asset management.…”

Section: Residual Minimizationmentioning

confidence: 99%

Methodology Maps for Model-Based Sensor-Data Interpretation to Support Civil-Infrastructure Management

Pai¹,

Smith

2022

Front. Built Environ.

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With increasing urbanization and depleting reserves of raw materials for construction, sustainable management of existing infrastructure will be an important challenge in this century. Structural sensing has the potential to increase knowledge of infrastructure behavior and improve engineering decision making for asset management. Model-based methodologies such as residual minimization (RM), Bayesian model updating (BMU) and error-domain model falsification (EDMF) have been proposed to interpret monitoring data and support asset management. Application of these methodologies requires approximations and assumptions related to model class, model complexity and uncertainty estimations, which ultimately affect the accuracy of data interpretation and subsequent decision making. This paper introduces methodology maps in order to provide guidance for appropriate use of these methodologies. The development of these maps is supported by in-house evaluations of nineteen full-scale cases since 2016 and a two-decade assessment of applications of model-based methodologies. Nineteen full-scale studies include structural identification, fatigue-life assessment, post-seismic risk assessment and geotechnical-excavation risk quantification. In some cases, much, previously unknown, reserve capacity has been quantified. RM and BMU may be useful for model-based data interpretation when uncertainty assumptions and computational constraints are satisfied. EDMF is a special implementation of BMU. It is more compatible with usual uncertainty characteristics, the nature of typically available engineering knowledge and infrastructure evaluation concepts than other methodologies. EDMF is most applicable to contexts of high magnitudes of uncertainties, including significant levels of model bias and other sources of systematic uncertainty. EDMF also provides additional practical advantages due to its ease of use and flexibility when information changes. In this paper, such observations have been leveraged to develop methodology maps. These maps guide users when selecting appropriate methodologies to interpret monitoring information through reference to uncertainty conditions and computational constraints. This improves asset-management decision making. These maps are thus expected to lead to lower maintenance costs and more sustainable infrastructure compared with current practice.

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Section: Residual Minimizationmentioning

confidence: 99%

Methodology Maps for Model-Based Sensor-Data Interpretation to Support Civil-Infrastructure Management

Pai¹,

Smith

2022

Front. Built Environ.

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“…Where ui is displacement calculated by finite element method in point i; ui* is displacement measured by monitoring in point i; and N is the number of measuring points for a vertical section of wall [15].…”

Section: Error Functionmentioning

confidence: 99%

Numerical Comparison of the Performance of Genetic Algorithm and Particle Swarm Optimization in Excavations

Hashemi

Rahmani²

2018

CivileJournal

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Today, the back analysis methods are known as reliable and effective approaches for estimating the soil strength parameters in the site of project. The back analysis can be performed by genetic algorithm and particle swarm optimization in the form of an optimization process. In this paper, the back analysis is carried out using genetic algorithm and particle swarm optimization in order to determine the soil strength parameters in an excavation project in Tehran city. The process is automatically accomplished by linking between MATLAB and Abaqus software using Python programming language. To assess the results of numerical method, this method is initially compared with the results of numerical studies by Babu and Singh. After the verification of numerical results, the values of the three parameters of elastic modulus, cohesion and friction angle (parameters of the Mohr–Coulomb model) of the soil are determined and optimized for three soil layers of the project site using genetic algorithm and particle swarm optimization. The results optimized by genetic algorithm and particle swarm optimization show a decrease of 72.1% and 62.4% in displacement differences in the results of project monitoring and numerical analysis, respectively. This research shows the better performance of genetic algorithm than particle swarm optimization in minimization of error and faster success in achieving termination conditions.

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“…Numerous efforts have done to predict these parameters using numerical modelling. However, Hashemi and Rahmani [1], stated that there is a limitation associated with the numerical modelling (e.g. finite element method) related to mechanical properties of the soil.…”

Section: Introductionmentioning

confidence: 99%

Prediction the shear strength and shear modulus of sand-clay mixture using bender element

Alshameri¹

2022

J Appl Eng Science

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Employing the conventional laboratory geotechnical methods such as shear box test to measure shear strength and shear modulus require destroying the samples which is seen as time consuming and costly. Whilst the bender element technique (BE) maintains the sample condition, time, and cost efficiency. Several sand-clay mixtures were compacted and subjected to bender element test as well as sheared using shear box test to measure and correlate shear modulus (t), shear strength (G) and the maximum shear modulus (Gmax). The results showed the critical stage (transition fines-grained) at fine-grained (FG) equal to 50% where any further increment beyond this value led to decrement the soil mixture strength. Both t and G were normalized using moisture content, density, and applied normal stress. Five empirical equations from the normalized shear strength tN were applied on the previous field data to exam their reliable and limitations. The equations indicated the importance of including the effect of overburden pressure for the natural sample as well as the in-situ moisture content and field density to avoid uncertainty in the predicted value of the soil shear strength and modulus. At no depth limitation, all empirical equations (tN1, tN2, tN3, tN4, and tN5) exceed ±20% lines which indicated a large variation in results. At depth limitation (< 5 m), only one equation corresponding to N4 showed reasonable validity and reliability to predict the shear strength. Similar was on the prediction of the shear modulus. The 5 m depth limit was recommended to apply the equation consistently.

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Determination of Multilayer Soil Strength Parameters Using Genetic Algorithm

Cited by 5 publications

References 15 publications

Methodology Maps for Model-Based Sensor-Data Interpretation to Support Civil-Infrastructure Management

Methodology Maps for Model-Based Sensor-Data Interpretation to Support Civil-Infrastructure Management

Numerical Comparison of the Performance of Genetic Algorithm and Particle Swarm Optimization in Excavations

Prediction the shear strength and shear modulus of sand-clay mixture using bender element

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