Use of artificial neural network to evaluate the vibration mitigation performance of geofoam-filled trenches

Jayawardana, Pubudu; Thambiratnam, David; Perera, Nimal; Chan, Tommy H.T.; Silva, G.H.M.J. Subashi De

doi:10.1016/j.sandf.2019.03.004

Cited by 14 publications

(8 citation statements)

References 26 publications

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“…Furthermore, the MLP utilizes a supervised learning technique known as backpropagation algorithm for the training of an input-output dataset, which involves the tuning of the parameter [89][90][91][92][93]. The tuning of the parameters, which are the weights and biases, are performed so that the error in the analysis can be minimized in the development of the model.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Statistical analysis and prediction of spatial resilient modulus of coarse-grained soils for pavement subbase and base layers using MLR, ANN and Ensemble techniques

Ikeagwuani

Nwonu

2022

Innov. Infrastruct. Solut.

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Prediction methods for the estimation of the resilient modulus( ) of unbound granular materials continues to be fraught with challenges associated with the in situ dependency of the existing methods, which makes them unreliable when considering the spatial variability of soil properties. Moreover, the artificial intelligence methods, which prove to be superior for prediction, indicate that more recent and hybrid machine learning methods are more apt. Consequently, a comparative analysis was executed in the present study using advanced statistical approaches and a simple routine method for the prediction of the of coarse-grained soils used as unbound granular materials in highway pavements. This study considered also the spatial variability of the routine soil properties based on the long-term pavement performance database. The result of the descriptive analysis conducted using principal component analysis revealed that the variation in is due to three principal factors, which include the effect of moisture on the fines content, effect of coarse particles on the compaction characteristics and the effect of the soil stress state. Subsequently, prediction of the using multiple linear regression was unreliable based on residual analysis and multi-collinearity problems. Thus, predictive modeling of was then executed using artificial neural network (ANN) and ensemble techniques (random forest and gradient boosting machine), which resulted in very high R 2 values above 0.9, considered apt for MEPD. The ANN model was found to provide a superior prediction performance than the ensemble techniques.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Statistical analysis and prediction of spatial resilient modulus of coarse-grained soils for pavement subbase and base layers using MLR, ANN and Ensemble techniques

Ikeagwuani

Nwonu

2022

Innov. Infrastruct. Solut.

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“…Rayleigh damping coefficients, which provide a linear material attenuation, were used to apply material damping to the model in Abaqus. Rayleigh damping is given in Equation 1, where [C], [M], and [K] indicate the damping matrix, mass matrix, and stiffness matrix, respectively (Jayawardana et al, 2019 [12]).…”

Section: Soil Types and Materials Propertiesmentioning

confidence: 99%

Effect of Soil Types on Attenuation of Piled Induced Ground Vibration: Experimental and Numerical Study

2022

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Ground vibration induced by pile driving has become a major concern in the construction industry, mainly due to its negative impact on the structural health of surrounding structures. Therefore, it is necessary to estimate the level of vibration propagating to surroundings before processing the pile driving to avoid any adverse impact. Pile driving is usually done in different types of soils. However, experimental evaluation of ground vibration propagation and attenuation through different soil types is not feasible, both technically and economically. The objective of the current study is to investigate the ground vibration propagation and attenuation through different types of soil by using a numerical model, which is validated with experimental measurements.A two-dimensional axisymmetric Finite Element Model (FEM) was developed by using Abaqus/CAE software. The boundary condition of the model was addressed by illustrating the application of nonreflecting boundary and fixed boundary. To minimize excessive mesh distortion during pile penetration into the soil, Arbitrary Lagrangian-Eulerian (ALE) adaptive mesh was used. The model was validated by comparing Peak Particle Velocities (PPVs) obtained from a full-scale field experiment for peat soil and laterite soil. The validated model was used to predict the ground vibration propagation and attenuation through different soil types: loess, silt, and sandy clay. A rate of vibration attenuation over a distance of 9 m was found to be 1.14 mms -1 /m, 0.66 mms -1 /m, 0.572 mms -1 /m, 0.5 mms -1 /m, and 0.41 mms -1 /m for laterite, loess, sandy clay, silt, and peat, respectively, indicating that among these five different soil types, the highest ground vibration attenuation was revealed by laterite soil while the least ground vibration attenuation was revealed by peat soil.

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“…ANN are widely used in the study of soil vibrations and earthquakes [45][46][47][48][49][50][51][52][53].…”

Section: Soil Mechanicsmentioning

confidence: 99%

“…In particular, neural networks are used for predictive tunneling-induced ground movements in tunneling projects [48], for evaluating the effectiveness of mitigating ground vibration with dual or two trenches [45], for predicting the effectiveness of geofoam-filled trenches to mitigate ground vibration [47], for predicting the ground settlement along railways induced by an excavation [46], assessment of free-field vibrations due to railway traffic [51], for reliability-based cost optimization of seismically isolated structures [49] and others.…”

Section: Soil Mechanicsmentioning

confidence: 99%

Applying Artificial Neural Networks In Construction

Doroshenko

2020

E3S Web Conf.

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Currently, artificial neural networks (ANN) are used to solve the following complex problems: pattern recognition, speech recognition, complex forecasts and others. The main applications of ANN are decision making, pattern recognition, optimization, forecasting, data analysis. This paper presents an overview of applications of ANN in construction industry, including energy efficiency and energy consumption, structural analysis, construction materials, smart city and BIM technologies, structural design and optimization, application forecasting, construction engineering and soil mechanics.

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Use of artificial neural network to evaluate the vibration mitigation performance of geofoam-filled trenches

Cited by 14 publications

References 26 publications

Statistical analysis and prediction of spatial resilient modulus of coarse-grained soils for pavement subbase and base layers using MLR, ANN and Ensemble techniques

Statistical analysis and prediction of spatial resilient modulus of coarse-grained soils for pavement subbase and base layers using MLR, ANN and Ensemble techniques

Effect of Soil Types on Attenuation of Piled Induced Ground Vibration: Experimental and Numerical Study

Applying Artificial Neural Networks In Construction

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