A review of Genetic Programming and Artificial Neural Network applications in pile foundations

Fatehnia, Milad; Amirinia, Gholamreza

doi:10.1186/s40703-017-0067-6

Cited by 41 publications

(11 citation statements)

References 43 publications

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“…Using the TDR probe with 0.64-mm-thick PVC insulation, the effective dielectric constants of the dry sand; wet sands with gravimetric water contents of 10%, 20%, and 30%; and clay were 1.69; 2.62, 4.33, and 6.05; and 6.70, respectively. (1) TDR probe without PVC insulation. (2) TDR probe with PVC insulation of thickness 0.27 mm.…”

Section: Construction Of Model Pilementioning

confidence: 99%

“…In measurements, the noise variance (σ 2 ) was approximately 1.9, and the signal-to-noise ratio was improved by stacking 200 signals. (1) TDR probe without PVC insulation. (2) TDR probe with PVC insulation of thickness 0.27 mm.…”

Section: Generation and Detection Of Electromagnetic Wavesmentioning

confidence: 99%

“…A bored pile is a reinforced-concrete foundation that supports a superstructure by transferring vertical loads into the ground [ 1 ]. Bored piles are subjected to lateral loads by soil pressure, in addition to vertical loads [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

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Variations in Velocity and Sensitivity of Electromagnetic Waves in Transmission Lines Configured in Model Piles with Necking Defects Containing Soils

Kim

Lee

2020

Sensors

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This study investigates variations in the velocity and sensitivity of electromagnetic waves in transmission lines configured in defective model piles for the detection of necking defects containing soil. Experiments are performed with model piles containing defects filled with different materials, such as air, sands, and clay. Five different types of transmission lines are configured in model piles. The electromagnetic waves are generated and detected using a time domain reflectometer. The velocity of electromagnetic waves is highest when the defect is filled with air, and it decreases with an increase in the water content. The velocity is lowest when the defect is filled with clay. The sensitivity of transmission lines for detecting defects decreases with an increase in soil water contents. The transmission line with a single electrical wire and epoxy-coated rebar exhibits the highest sensitivity, followed by that with three and two parallel electrical wires. Transmission lines with a single electrical wire and uncoated rebar and those with two parallel electrical wires wrapped with a sheath exhibit poor sensitivity when the defect is filled with clay. This study demonstrates that electromagnetic waves can be effective tools for detecting necking defects with wet and conductive soils in bored piles.

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Section: Construction Of Model Pilementioning

confidence: 99%

Section: Generation and Detection Of Electromagnetic Wavesmentioning

confidence: 99%

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Variations in Velocity and Sensitivity of Electromagnetic Waves in Transmission Lines Configured in Model Piles with Necking Defects Containing Soils

Kim

Lee

2020

Sensors

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“…Kockelman and Kweon ( 5 ) applied ordered probit models using single- and two-vehicle crash data to investigate the risk parameters influencing the probabilities of a crash resulting in a severe injury or fatality. Data mining methods have recently been applied in different engineering fields ( 43 – 45 ) including traffic safety. Some studies utilized Artificial Neural Networks (ANN) to estimate injury severity ( 10 , 11 , 22 , 46 – 48 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Methods to Analyze Injury Severity of Drivers from Different Age and Gender Groups

Mafi

AbdelRazig

Doczy

2018

Transportation Research Record

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Access to non-biased and accurate models capable of predicting driver injury severity of collision events is vital for determining what safety measures should be implemented at intersections. Inadequate models can underestimate the potential for collision events to result in driver fatalities or injuries, which can lead to improperly assessing the safety criteria of an intersection. This study investigates how injury severity differs between drivers of various ages and gender groups using cost-sensitive data-mining models. Previous research efforts have used machine learning methods for predicting injury severity; however, these studies did not consider the consequences (cost) of incorrect predictions. This paper addresses this shortfall by considering the monetary cost of incorrect injury severity predictions when developing C4.5, instance-based (IB), and random forest (RF) machine-learning models. One model of each method was developed for four distinct cohorts of drivers (i.e., younger males, younger females, older males, and older females). Each model considered a selection of driver, vehicular, road/traffic, environmental, and crash parameters for determining if they significantly influenced driver injury severity. A five-year period of two-vehicle crash data collected at signalized intersections in the metropolitan area of Miami, Florida was used in the models. Results indicated that cost-sensitive learning classifiers were superior to regular classifiers at accurately predicting injuries and fatalities of crashes. Among cost-sensitive models, RF outperformed C4.5 and IB models in predicting driver injury severity for four groups of drivers. The models displayed substantial differences in injury severity determinants across the age/gender cohorts.

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“…Contrarily, data mining models are non-parametric tools that do not require any pre-established correlations among the dependent variable and predictors, while being able to manage outliers and missing values. Data mining techniques have been used in different engineering problems (16)(17)(18). Recently, researchers have employed these techniques for predicting drivers' gap decisions, which are comparable or sometimes superior to statistical models.…”

mentioning

confidence: 99%

Analysis of Gap Acceptance Behavior for Unprotected Right and Left Turning Maneuvers at Signalized Intersections using Data Mining Methods: A Driving Simulation Approach

Mafi

AbdelRazig

Doczy

2018

Transportation Research Record

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Gap acceptance predictability has become a vital area of interest for traffic safety and operations due to its complexity and significance in understanding a population’s driving behavior. Recent studies have implemented statistical modeling techniques, such as binary logit model (BLM), to predict drivers’ gap acceptance behaviors. However, these models have inherent presumptions and pre-set correlations that, if contravened, can produce erroneous results. The use of non-parametric data mining techniques, such as decision trees, avoids these deficiencies, thus resulting in improvements to the predictive capability of the models. In this study, the feasibility of C4.5 decision trees, instance-based (IB), and random forest (RF) models for predicting drivers’ gap decisions was examined by comparing their results with BLM. To accomplish this objective, 66 study participants drove through ten driving simulation scenarios requiring the navigation of unprotected right and left-turning maneuvers at four-legged, signalized intersections. The data collected from these tests will provide means to directly compare and rank the data mining and statistical models, while also allowing for the identification of variables that are significantly influencing gap acceptance. Results produced from the models indicated that data mining models were superior to BLM at accurately predicting a participant’s gap decisions. RF models outperformed the C4.5 and IB models in predicting gap acceptance behaviors for both the left and right turning scenarios. Because of its superior performance, the authors recommend the implementation of the RF model for predicting gap decisions at unprotected signalized intersections.

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A review of Genetic Programming and Artificial Neural Network applications in pile foundations

Cited by 41 publications

References 43 publications

Variations in Velocity and Sensitivity of Electromagnetic Waves in Transmission Lines Configured in Model Piles with Necking Defects Containing Soils

Variations in Velocity and Sensitivity of Electromagnetic Waves in Transmission Lines Configured in Model Piles with Necking Defects Containing Soils

Machine Learning Methods to Analyze Injury Severity of Drivers from Different Age and Gender Groups

Analysis of Gap Acceptance Behavior for Unprotected Right and Left Turning Maneuvers at Signalized Intersections using Data Mining Methods: A Driving Simulation Approach

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