Integrating the LSSVM and RBFNN models with three optimization algorithms to predict the soil liquefaction potential

Cai, Mingxiang; Ouaer, Hocine; Mohammed, Ahmed; Chen, Xiaoling; Amar, Menad Nait; Hasanipanah, Mahdi

doi:10.1007/s00366-021-01392-w

Cited by 48 publications

(13 citation statements)

References 82 publications

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“…In this study, a water reducer polymer with one linear backbone consists of side groups of carboxylates (polycarboxylate ether-based polymer). The water reducer polymer is used to treat and change the behavior of cement using carboxylate groups as anchoring groups [10][11][12][13][14]. With a pH of 10, the solid content of the water reducer polymer is larger than 97% (data provided from the supplier).…”

Section: Methodsmentioning

confidence: 99%

Modeling Flexural and Compressive Strengths Behaviour of Cement-Grouted Sands Modified with Water Reducer Polymer

et al. 2022

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By using the American Society for Testing and Materials and British Standards standards, the impact of various grading of sand (Five types of sand) on the compressive strength (CS) of the cement grout (CG) treated with water reducer polymer is investigated. The properties of CG treated with polymer up to 0.16 % of cement weight were investigated and quantified in both fresh and hardened states. The water to cement ratio (w/c) was reduced by 21.9 to 54.1%, and the CG flow time was retained between 18 and 23 seconds. The highest compression strength was achieved at seven and 28 days for the cement-grouted sand using the coarser-graded sand than finer-graded sand at low w/c ranged between 0.50 and 0.53. The highest compression strength was obtained at high w/c for the cement grout mixed with the fine-grained sands compared to coarse-grained sands. Adding water reducer polymer enhances the compressive strength (spc) and cylindrical compressive strength (scc) by 113 to 577% and 53 to 459%, depending on mix proportion and curing period. An amorphous gel fills the porous places between the cement particles were formed when the CG was treated with water reducer polymer, which reduces voids, increases porosity, and increases the cement's dry density; as a result, the CS of the CG increases significantly. To evaluate the CS of CG with different grain sizes, w/c, percentage of polymer, and curing age, linear and nonlinear techniques were used. according to the bs standard, the CS of the CG produced was 71% higher than that of the identical mix produced according to the ASTM standard. Compared to the other sands, the cement grout produced with finer sand grading had the maximum flexural strength at all testing ages.

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Section: Methodsmentioning

confidence: 99%

Modeling Flexural and Compressive Strengths Behaviour of Cement-Grouted Sands Modified with Water Reducer Polymer

et al. 2022

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“…In addition, plasticity index (PI) has considerable influence on measuring the liquefaction behavior of fine-grained soils on the liquefaction sensitivity of highly plastic soils. Ghani [137,138] and others compared Culture Algorithm (CA), Firefly Algorithm (FA), Genetic Algorithm (GA), Gray Wolf Optimizer (GWO), Particle Swarm Optimization (PSO) and Gradient-based optimizer (GBO) combined with artificial neural network for soil liquefaction assessment and concluded that PI and GBO based ANN models are a promising new tool and can help geotechnical engineers to be able to estimate the occurrence of liquefaction in the early stages of engineering projects.…”

Section: Prediction Of Seismic Liquefactionmentioning

confidence: 99%

Recent Advances of Deep Learning in Geological Hazard Forecasting

Wang¹,

Sun²,

Chen³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Geological hazard is an adverse geological condition that can cause loss of life and property. Accurate prediction and analysis of geological hazards is an important and challenging task. In the past decade, there has been a great expansion of geohazard detection data and advancement in data-driven simulation techniques. In particular, great efforts have been made in applying deep learning to predict geohazards. To understand the recent progress in this field, this paper provides an overview of the commonly used data sources and deep neural networks in the prediction of a variety of geological hazards. KEYWORDSGeological hazard; deep learning; neural networks; geohazard data sources; earthquake; volcanic 1 Introduction Geological hazards are caused by endogenous and exogenous geological processes or anthropogenic factors on earth [1,2], which encompass a broad range of abnormal stratigraphic activity or extreme changes in geological environments, such as landslides, avalanches, debris flows, earthquakes, etc. Geological hazards pose a serious threat to human life and property [3]. Only by accurately forecasting the occurrence of geohazards can emergency measures be devised to mitigate the damage caused by the impending disaster. Geological hazard forecasting refers to the use of logical reasoning, numerical simulation, and comprehensive analysis based on historical geological hazard activity patterns, formation conditions, occurrence mechanisms, and other factors to speculate and assess the development and changes of geological hazards and the possible degree of danger and damage in a certain period in the future. Geohazard forecasting has attracted tremendous attention nowadays, particularly considering natural resource scarcity, environmental degradation, population expansion, sustainable development, and world economic integration. Many efforts have been made in forecasting geological hazards in the past decades. The "3S technology", including Global Position System (GPS), Geographic Information System (GIS) [4], and Remote Sensing (RS), has been widely applied in the field of geological hazard forecasting [5]. The

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“…The MAE variations with the number of epochs are presented for the preliminarily designed networks. After designing the optimum architecture, the available data set (total of 80 data) was divided into two parts: the first part was 2/3 of the overall data set (57) for training the network; the second part was 1/3 of the total data set (27) for testing the network [20]. Several transfer functions and ANN structures with a varied number of hidden layers and neurons were tested to design the optimal network structure to predict the concrete compressive strength.…”

Section: Intelligence Neural Network (Ann)mentioning

confidence: 99%

“…Various approaches for modeling material properties have been used to predict the flowability [20,21] and strength of concrete [22,23] drilling fluids and mortar [24,25], including numerical simulations, statistical methods, and recently established systematic methods such as regression analysis, M5-Ptree multilinear, and ANN model [26]. Validating experimental results using different model techniques has been successfully used to estimate material properties using linear, nonlinear, and intelligent neural networks in various fields such as soils [26], rocks [27], hydraulic fracturing [28], oil well cement [29], and concrete to save time and reduce project costs [30]. The slump value range was 125-145 mm for the concrete samples modified with polymers.…”

Section: Introductionmentioning

confidence: 99%

Interpreting the experimental results of compressive strength of hand-mixed cement-grouted sands using various mathematical approaches

Mahmood

Mohammed

Sihag

et al. 2021

Archiv.Civ.Mech.Eng

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Using two different test standards (ASTM and BS), the influence of five different sizes of sand on the ultimate stress (MPa) of hand-mixed cement-grouted sands modified with polymer is discussed in this study. The characteristics of cement-grouted sands modified with polymer up to 0.16% (percent weight of dry cement) were evaluated and measured in fresh and hardened conditions. Adding polymer decreased the water/cement ratio ( w∕c ) from 0.6 to 0.5, and it kept the flow time of the cement-based grout in the range of 18 to 23 s recommended by ASTM standard. Using mix proportion and curing time, adding polymer significantly increased the prismatic and cylindrical compressive strength (MPa) by 113 to 577% and 53 to 459%. Several mathematical approaches such as linear regression (LR), Nonlinear regression (NLR), multilinear regression (MLR), Artificial neural network (ANN), and M5P-tree were used to predict the compression strength of cement-grouted sand with a different size of sand, w/c, polymer content, and curing age. Based on the scatter index (SI), objective function (OBJ) assessments, and training and testing datasets, the compressive strength of the cement-grouted sands can be predicted well using NLR and ANN models. The compression strength tested using the BS standard was 71% higher than the compression strength of the same mix tested using the ASTM standard.

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Integrating the LSSVM and RBFNN models with three optimization algorithms to predict the soil liquefaction potential

Cited by 48 publications

References 82 publications

Modeling Flexural and Compressive Strengths Behaviour of Cement-Grouted Sands Modified with Water Reducer Polymer

Modeling Flexural and Compressive Strengths Behaviour of Cement-Grouted Sands Modified with Water Reducer Polymer

Recent Advances of Deep Learning in Geological Hazard Forecasting

Interpreting the experimental results of compressive strength of hand-mixed cement-grouted sands using various mathematical approaches

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