Neural Network Algorithms for the Correction of Concrete Slab Stresses from Linear Elastic Layered Programs

1997 ◽  
Vol 1568 (1) ◽  
pp. 44-51 ◽  
Author(s):  
Louis D. Haussmann ◽  
Erol Tutumluer ◽  
Ernest J. Barenberg
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Load Transfer ◽  
Concrete Slab ◽  
Loading Conditions ◽  
Ann Model ◽  
Portland Cement Concrete ◽  
Element Analysis ◽  
Wide Range ◽  
Artificial Neural Network Ann

Elastic layered programs (ELPs) are currently being used in mechanistic-based pavement design procedures for the analysis of jointed portland cement concrete pavements. Corrections must be made to stresses obtained from ELP solutions to account for the effects of finite slab size, load location on the slab, and load transfer efficiency of the joints. A preliminary artificial neural network (ANN) model is trained and used as a tool to predict the results of a finite-element analysis program for a standard pavement section. Under identical loading conditions, the trained neural network produces stresses within 1.2 percent of those obtained from finite-element analyses. The trained ANN model is found to be very effective for correcting ELP stresses, practically in the blink of an eye, with no requirements of complicated finite-element inputs. The preliminary ANN algorithm is currently being expanded to handle more general input conditions covering a wide range of slab sizes, slab thicknesses, subgrade supports, and loading conditions. Design curves created from these ANN algorithms will eventually enable pavement engineers to easily incorporate sophisticated state-of-the-art technology into routine practical design.

Vibration–Based Damage Detection Of Slab Structure Using Artificial Neural Network

2012 ◽  
Author(s):  
Norhisham Bakhary
Keyword(s):  
Neural Network ◽  
Finite Element Analysis ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Damage Detection ◽  
Ann Model ◽  
Element Analysis ◽  
Severity Prediction ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Kertas kerja ini memaparkan kajian berkenaan keberkesanan Artificial Neural Network (ANN) dalam mengenal pasti kerosakan di dalam struktur. Data dari getaran seperti frekuensi semula jadi dan mod bentuk digunakan sebagai data masukan bagi ANN untuk meramalkan lokasi dan tahap kerosakan bagi struktur lantai. Analisis unsur terhingga (Finite Element Analysis) telah digunakan untuk memperoleh ciri–ciri dinamik bagi struktur–struktur rosak dan tidak rosak untuk ‘melatih’ model ‘neural network’. Senario kerosakan yang berbeza disimulasikan dengan mengurangkan kekukuhan elemen pada lokasi yang berbeza sepanjang struktur tersebut. Berbagai kombinasi data masukan bagi mod yang berbeza telah digunakan untuk memperolehi model ANN yang terbaik. Hasil kajian ini menunjukkan ANN mampu memberikan keputusan yang baik dalam meramal kerosakan pada struktur lantai tersebut. Kata kunci: Ramalan kerosakan struktur, Artificial Neural Network This paper investigates the effectiveness of artificial neural network (ANN) in identifying damages in structures. Global (natural frequencies) and local (mode shapes) vibration–based data has been used as the input to ANN for location and severity prediction of damages in a slab–like structure. A finite element analysis has been used to obtain the dynamic characteristics of intact and damaged structure to train the neural network model. Different damage scenarios have been introduced by reducing the local stiffness of the selected elements at different locations along the structure. Several combinations of input variables in different modes have been used in order to obtain a reliable ANN model. The trained ANN model is validated using laboratory test data. The results show that ANN is capable of providing acceptable result on damage prediction of tested slab structure. Key words: Structural damage detection, artificial neural network

scholarly journals Failure Pressure Prediction of a Corroded Pipeline with Longitudinally Interacting Corrosion Defects Subjected to Combined Loadings Using FEM and ANN

2021 ◽  
Vol 9 (3) ◽  
pp. 281
Author(s):  
Michael Lo ◽  
Saravanan Karuppanan ◽  
Mark Ovinis
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Training Data ◽  
Percentage Error ◽  
Learning Tools ◽  
Ann Model ◽  
Element Analysis ◽  
Failure Pressure ◽  
Corrosion Defects ◽  
Artificial Neural Network Ann

Machine learning tools are increasingly adopted in various industries because of their excellent predictive capability, with high precision and high accuracy. In this work, analytical equations to predict the failure pressure of a corroded pipeline with longitudinally interacting corrosion defects subjected to combined loads of internal pressure and longitudinal compressive stress were derived, based on an artificial neural network (ANN) model trained with data obtained from the finite element method (FEM). The FEM was validated against full-scale burst tests and subsequently used to simulate the failure of a pipeline with various corrosion geometric parameters and loadings. The results from the finite element analysis (FEA) were also compared with the Det Norske Veritas (DNV-RP-F101) method. The ANN model was developed based on the training data from FEA and its performance was evaluated after the model was trained. Analytical equations to predict the failure pressure were derived based on the weights and biases of the trained neural network. The equations have a good correlation value, with an R2 of 0.9921, with the percentage error ranging from −9.39% to 4.63%, when compared with FEA results.

Using Artificial Neural Network (ANN) for Manipulating Energy Gain of Nansulate Coating

2011 ◽  
Vol 2 (1) ◽  
Author(s):  
Hadi Salehi ◽  
Mosayyeb Amiri ◽  
Morteza Esfandyari
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Energy Gain ◽  
Operating Conditions ◽  
Maximum Relative Error ◽  
Ann Model ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this work, an extensive experimental data of Nansulate coating from NanoTechInc were applied to develop an artificial neural network (ANN) model. The Levenberg–Marquart algorithm has been used in network training to predict and calculate the energy gain and energy saving of Nansulate coating. By comparing the obtained results from ANN model with experimental data, it was observed that there is more qualitative and quantitative agreement between ANN model values and experimental data results. Furthermore, the developed ANN model shows more accurate prediction over a wide range of operating conditions. Also, maximum relative error of 3% was observed by comparison of experimental and ANN simulation results.

scholarly journals Method for determining load magnitude and location from the plastic deformation of fixed beams using a neural network

2021 ◽  
Vol 104 (1) ◽  
pp. 003685042110033
Author(s):  
Junqing Yin ◽  
Jinyu Gu ◽  
Yongdang Chen ◽  
Wenbin Tang ◽  
Feng Zhang
Keyword(s):  
Neural Network ◽  
Plastic Deformation ◽  
Finite Element ◽  
Element Model ◽  
Training Data ◽  
Ann Model ◽  
Element Analysis ◽  
Data Set ◽  
Sample Set ◽  
Fixed Beam

Fixed beam structures are widely used in engineering, and a common problem is determining the load conditions of these structures resulting from impact loads. In this study, a method for accurately identifying the location and magnitude of the load causing plastic deformation of a fixed beam using a backpropagation artificial neural network (BP-ANN). First, a load of known location and magnitude is applied to the finite element model of a fixed beam to create plastic deformation, and a polynomial expression is used to fit the resulting deformed shape. A basic data set was established through this method for a series of calculations, and it consists of the location and magnitude of the applied load and polynomial coefficients. Then, a BP-ANN model for expanding the sample data is established and the sample set is expanded to solve the common problem of insufficient samples. Finally, using the extended sample set as training data, the coefficients of the polynomial function describing the plastic deformation of the fixed beam are used as input data, the position and magnitude of the load are used as output data, a BP-ANN prediction model is established. The prediction results are compared with the results of finite element analysis to verify the effectiveness of the method.

scholarly journals Predicting Nonlinear and Anisotropic Mechanics of Metal Rubber Using a Combination of Constitutive Modeling, Machine Learning, and Finite Element Analysis

Materials ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5200
Author(s):  
Yalei Zhao ◽  
Hui Yan ◽  
Yiming Wang ◽  
Tianyi Jiang ◽  
Hongyuan Jiang
Keyword(s):  
Finite Element ◽  
Constitutive Modeling ◽  
Constitutive Models ◽  
Finite Element Method Simulation ◽  
Ann Model ◽  
The Finite Element Method ◽  
Functional Material ◽  
Element Analysis ◽  
Metal Rubber ◽  
Artificial Neural Network Ann

Metal rubber (MR) is an entangled fibrous functional material, and its mechanical properties are crucial for its applications; however, numerical constitutive models of MR for prediction and calculation are currently undeveloped. In this work, we provide a numerical constitutive model to express the mechanics of MR materials and develop an efficient finite elements method (FEM) to calculate the performance of MR components. We analyze the nonlinearity and anisotropy characteristics of MR during the deformation process. The elasticity matrix is adopted to express the nonlinearity and anisotropy of MR. An artificial neural network (ANN) model is built, trained, and tested to output the current elastic moduli for the elasticity matrix. Then, we combine the constitutive ANN model with the finite element method simulation to calculate the mechanics of the MR component. Finally, we perform a series of static and shock experiments and finite element simulations of an MR isolator. The results demonstrate the feasibility and accuracy of the numerical constitutive MR model. This work provides an efficient and convenient method for the design and analysis of MR components.

Three-Dimensional Finite Element Analysis of Doweled Joints for Airport Pavements

2003 ◽  
Vol 1853 (1) ◽  
pp. 100-109 ◽  
Author(s):  
Jiwon Kim ◽  
Keith D. Hjelmstad
Keyword(s):  
Finite Element ◽  
Load Transfer ◽  
Concrete Slab ◽  
Three Dimensional ◽  
Nonlinear Material ◽  
Structural Behavior ◽  
Loading Conditions ◽  
Dimensional Finite Element ◽  
Three Dimensional Finite Element ◽  
Analytical Approaches

Various aspects of the structural behavior of doweled joints, including load transfer, in rigid airport pavement systems are investigated by using nonlinear three-dimensional finite element methods. The finite element models include two concrete slab segments connected by dowels. The concrete slab and supporting layers are simulated by continuum solid elements. Solid elements can capture the severe local deformation in the concrete slab in the vicinity of wheel loads. They allow the modeling of nonlinear material response of the supporting layers and of frictional contact between the concrete slabs and supporting layers. These features generally are not considered in classical analytical approaches. The structural behavior of the doweled joint is investigated for various design and loading conditions, including tire pressure, slab thickness, dowel looseness, and different landing gear configurations. An attempt is made to quantify the amount and efficiency of load transfer through the dowels. According to the finite element results, 15% to 30% of the applied wheel load is transferred to the adjacent slab segment by the dowels in an intact joint, depending on design and loading conditions. In addition, 95% of the transferred shear force is carried only by the 9 or 11 dowels that are closest to the applied load.

Three-Dimensional Finite Element Analysis of Doweled Joints in Concrete Pavements

2013 ◽  
Vol 723 ◽  
pp. 245-257
Author(s):  
How Bing Sii ◽  
Gary W. Chai ◽  
Rudi van Staden ◽  
Hong Guan
Keyword(s):  
Finite Element ◽  
Load Transfer ◽  
Concrete Slab ◽  
Heavy Traffic ◽  
Three Dimensional ◽  
Concrete Pavements ◽  
Structural Behaviour ◽  
Shear Forces ◽  
Element Analysis ◽  
Dowel Bar

Concrete pavements are usually selected by pavement engineers for roads subjected to heavy traffic loading and feature high maintenance and construction costs. As such, the structural behaviour of concrete pavements with doweled joints is evaluated herein using Finite Element Method. The pavement system is modelled using three-dimensional brick elements and five loading cases are applied to replicate realistic vehicular loadings approaching and leaving the joint. The structural behaviour of the pavement at the doweled joint is investigated for: (1) pavement with and without voids, and (2) different dowel bar spacing. The amount of load transfer was obtained from the shear force in the beam elements that simulate dowels. Results show that the voids underneath the joint causes an increase in the vertical displacement of the concrete slab and vertical stress at concrete/dowel bar interface which may result in crushing of the concrete and dowel loosening. Wider dowel spacings result in increased shear forces and the size of the region containing engaged dowels does not change significantly with dowel spacing, only effecting the distribution of shear forces. The study shows that the dowel bars perform effectively as a load transfer device in the concrete pavement system even under severe conditions.

scholarly journals Predicting the 14-Day Hospital Readmission of Patients with Pneumonia Using Artificial Neural Networks (ANN)

2021 ◽  
Vol 18 (10) ◽  
pp. 5110
Author(s):  
Shu-Farn Tey ◽  
Chung-Feng Liu ◽  
Tsair-Wei Chien ◽  
Chin-Wei Hsu ◽  
Kun-Chen Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Early Stage ◽  
Characteristic Curve ◽  
Patient Discharge ◽  
Ann Model ◽  
Healthcare Settings ◽  
High Area ◽  
Icd 10 ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Unplanned patient readmission (UPRA) is frequent and costly in healthcare settings. No indicators during hospitalization have been suggested to clinicians as useful for identifying patients at high risk of UPRA. This study aimed to create a prediction model for the early detection of 14-day UPRA of patients with pneumonia. We downloaded the data of patients with pneumonia as the primary disease (e.g., ICD-10:J12*-J18*) at three hospitals in Taiwan from 2016 to 2018. A total of 21,892 cases (1208 (6%) for UPRA) were collected. Two models, namely, artificial neural network (ANN) and convolutional neural network (CNN), were compared using the training (n = 15,324; ≅70%) and test (n = 6568; ≅30%) sets to verify the model accuracy. An app was developed for the prediction and classification of UPRA. We observed that (i) the 17 feature variables extracted in this study yielded a high area under the receiver operating characteristic curve of 0.75 using the ANN model and that (ii) the ANN exhibited better AUC (0.73) than the CNN (0.50), and (iii) a ready and available app for predicting UHA was developed. The app could help clinicians predict UPRA of patients with pneumonia at an early stage and enable them to formulate preparedness plans near or after patient discharge from hospitalization.

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