Artificial Neural Network Prediction of Airport Pavement Moduli Using Interpolated Surface Deflection Data

Abstract Establishing the structural integrity of an airport pavement is crucial to assess its remaining life and implement strategies or priorities for action. In this context, the elastic modulus represents an effective indicator of the condition of the pavement which can be calculated through back-calculation procedures starting from surface deflections, obtained from a non-destructive test (such as the Heavy Weight Deflectometer). Nevertheless, the conventional inverse engineering analysis involves the use of an axial-symmetric pavement finite-element program able to evaluate stiffness values exclusively at the deflection measuring points. This study presents an alternative methodology for spatial modelling of the load- bearing capacity of the runway surface pavement layer from deflection data measured at specific points, using Shallow Artificial Neural Networks. The search of the optimal neural model hyperparameters has been addressed through a Bayesian Optimization procedure and a 5-fold cross-validation has been implemented for a fair performance evaluation, given the limited number of deflection measures available. Once the optimal model has been defined, the measured surface deflection data were linearly interpolated and resampled gridding data were used as a new input matrix of the neural model to predict the expected value of elastic moduli at non-sampled points on the runway. The optimal BO model has returned very satisfactory results with a value of Pearson Coefficient R averaged over 5-fold equal to 0.96597 and of Mean Squared Error averaged over 5-fold equal to 0.01849. In such a way, a contour map of the runway stiffness has been drawn, to provide a support tool for the planning of intervention priorities.

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Analysis of Structural Integrity of a Building Using an Artificial Neural Network ARTMAP-Fuzzy-Wavelet

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1025-1026.1113 ◽

2014 ◽

Vol 1025-1026 ◽

pp. 1113-1118 ◽

Cited By ~ 4

Author(s):

Fernando Parra dos Anjos Lima ◽

Simone Silva Frutuoso de Souza ◽

Fábio Roberto Chavarette ◽

Mara Lúcia Martins Lopes ◽

Antonio Eduardo Turra ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Structural Integrity ◽

Fuzzy Artmap ◽

Base Line ◽

Building Structures ◽

Structural Failure ◽

Support Tool ◽

Artificial Neural

This paper presents an ARTMAP-Fuzzy-Wavelet artificial neural network to perform the analysis of the structural integrity of a building. The combination of Fuzzy ARTMAP neural network, wavelets transform to generate a tool that performs the identification and characterization of structural failure. This method is applied as a support tool for professionals in the inspection of mechanical and building structures to identify and characterize flaws in order to carry out preventive maintenance to ensure the integrity of the structure and decision making. In order to validate the methodology perform mathematical modeling of a building of two walk, and from this model were simulated different situations (base-line condition and improper conditions), yielding a database of signals that serve as input ARTMAP-Fuzzy-Wavelet neural network. The results obtained by ARTMAP-Fuzzy-Wavelet shown efficiency and robustness.

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Prediction and evaluation of photovoltaic-thermal energy systems production using artificial neural network and experimental dataset

10.36227/techrxiv.15152088 ◽

2021 ◽

Author(s):

Jabar Yousif

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Mean Squared Error ◽

Neural Model ◽

Ann Model ◽

Pv System ◽

Experimental Dataset ◽

Ann Models ◽

Artificial Neural ◽

T System

The current work aims to predict and assess a PV/T system using <a href="https://www.sciencedirect.com/topics/engineering/artificial-neural-network-model">ANN models</a> based on an experimental dataset in Oman. The PV/T system with weather station and data acquisition was installed in Sohar, Oman. The weather and electrical data has been recorded. A novel mathematical and ANN model for examining the performance of PV/T systems has been developed. The experimental results show improvement in PVT power production (68.6132 W) compared to the conventional PV (66.7827 W). The results demonstrate that the three proposed models (MLP, SOFM, and SVM) achieved excellent <a href="https://www.sciencedirect.com/topics/engineering/mean-squared-error">MSE</a> results for generating the current values of the PV system (0.00043, 0.00030, 0.00041) and PV/T system (0.00719, 0.00683, 0.00763), respectively. Also, the proposed models delivered excellent MSE results for simulating the power values of the PV system (0.04457, 0.05006, 0.13816) and PV/T system (0.04457, 0.05006, 0.13816), respectively. The proposed models result validated with experimental data using descriptive statistics and Evaluation Metrics. Finally, the proposed <a href="https://www.sciencedirect.com/topics/engineering/neural-model">neural models</a> can generate future figures for any needed period that accurately fit the actual datasets.

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Prediction and evaluation of photovoltaic-thermal energy systems production using artificial neural network and experimental dataset

10.36227/techrxiv.15152088.v1 ◽

2021 ◽

Author(s):

Jabar Yousif

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Mean Squared Error ◽

Neural Model ◽

Ann Model ◽

Pv System ◽

Experimental Dataset ◽

Ann Models ◽

Artificial Neural ◽

T System

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A Novel Computational Intelligence Approach for Coal Consumption Forecasting in Iran

Sustainability ◽

10.3390/su13147612 ◽

2021 ◽

Vol 13 (14) ◽

pp. 7612

Author(s):

Mahdis sadat Jalaee ◽

Alireza Shakibaei ◽

Amin GhasemiNejad ◽

Sayyed Abdolmajid Jalaee ◽

Reza Derakhshani

Keyword(s):

Hybrid Method ◽

Computational Intelligence ◽

Energy Demand ◽

Mean Squared Error ◽

Absolute Error ◽

Coal Consumption ◽

Squared Error ◽

Artificial Neural ◽

Socio Economic Variables ◽

Intelligence Approach

Coal as a fossil and non-renewable fuel is one of the most valuable energy minerals in the world with the largest volume reserves. Artificial neural networks (ANN), despite being one of the highest breakthroughs in the field of computational intelligence, has some significant disadvantages, such as slow training, susceptibility to falling into a local optimal points, sensitivity of initial weights, and bias. To overcome these shortcomings, this study presents an improved ANN structure, that is optimized by a proposed hybrid method. The aim of this study is to propose a novel hybrid method for predicting coal consumption in Iran based on socio-economic variables using the bat and grey wolf optimization algorithm with an artificial neural network (BGWAN). For this purpose, data from 1981 to 2019 have been used for modelling and testing the method. The available data are partly used to find the optimal or near-optimal values of the weighting parameters (1980–2014) and partly to test the model (2015–2019). The performance of the BGWAN is evaluated by mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), standard deviation error (STD), and correlation coefficient (R^2) between the output of the method and the actual dataset. The result of this study showed that BGWAN performance was excellent and proved its efficiency as a useful and reliable tool for monitoring coal consumption or energy demand in Iran.

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Prognosis of water levels in a moor groundwater system influenced by hydrology and water extraction using an artificial neural network

10.5194/egusphere-egu21-3013 ◽

2021 ◽

Author(s):

Sascha Flaig ◽

Timothy Praditia ◽

Alexander Kissinger ◽

Ulrich Lang ◽

Sergey Oladyshkin ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Mean Squared Error ◽

Meteorological Data ◽

Water Levels ◽

Typical Structure ◽

Water Abstraction ◽

Physical Knowledge ◽

Artificial Neural ◽

The Impact

In order to prevent possible negative impacts of water abstraction in an ecologically sensitive moor south of Munich (Germany), a &#8220;predictive control&#8221; scheme is in place. We design an artificial neural network (ANN) to provide predictions of moor water levels and to separate hydrological from anthropogenic effects. As the moor is a dynamic system, we adopt the &#8222;Long short-term memory&#8220; architecture.To find the best LSTM setup, we train, test and compare LSTMs with two different structures: (1) the non-recurrent one-to-one structure, where the series of inputs are accumulated and fed into the LSTM; and (2) the recurrent many-to-many structure, where inputs gradually enter the LSTM (including LSTM forecasts from previous forecast time steps). The outputs of our LSTMs then feed into a readout layer that converts the hidden states into water level predictions. We hypothesize that the recurrent structure is the better structure because it better resembles the typical structure of differential equations for dynamic systems, as they would usually be used for hydro(geo)logical systems. We evaluate the comparison with the mean squared error as test metric, and conclude that the recurrent many-to-many LSTM performs better for the analyzed complex situations. It also produces plausible predictions with reasonable accuracy for seven days prediction horizon.Furthermore, we analyze the impact of preprocessing meteorological data to evapotranspiration data using typical ETA models. Inserting knowledge into the LSTM in the form of ETA models (rather than implicitly having the LSTM learn the ETA relations) leads to superior prediction results. This finding aligns well with current ideas on physically-inspired machine learning.As an additional validation step, we investigate whether our ANN is able to correctly identify both anthropogenic and natural influences and their interaction. To this end, we investigate two comparable pumping events under different meteorological conditions. Results indicate that all individual and combined influences of input parameters on water levels can be represented well. The neural networks recognize correctly that the predominant precipitation and lower evapotranspiration during one pumping event leads to a lower decrease of the hydrograph.To further demonstrate the capability of the trained neural network, scenarios of pumping events are created and simulated.In conclusion, we show that more robust and accurate predictions of moor water levels can be obtained if available physical knowledge of the modeled system is used to design and train the neural network. The artificial neural network can be a useful instrument to assess the impact of water abstraction by quantifying the anthropogenic influence.

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Using Monte Carlo method to estimate the behavior of neural training between balanced and unbalanced data in classification of patterns

Artificial Intelligence Research ◽

10.5430/air.v7n2p1 ◽

2018 ◽

Vol 7 (2) ◽

pp. 1

Author(s):

Paulo Marcelo Tasinaffo ◽

Gildárcio Sousa Gonçalves ◽

Adilson Marques da Cunha ◽

Luiz Alberto Vieira Dias

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Monte Carlo ◽

Monte Carlo Method ◽

Mean Squared Error ◽

Classification Problem ◽

Computational Environment ◽

The Monte Carlo Method ◽

Proposed Model ◽

Artificial Neural

This paper proposes to develop a model-based Monte Carlo method for computationally determining the best mean squared error of training for an artificial neural network with feedforward architecture. It is applied for a particular non-linear classification problem of input/output patterns in a computational environment with abundant data. The Monte Carlo method allows computationally checking that balanced data are much better than non-balanced ones for an artificial neural network to learn by means of supervised learning. The major contribution of this investigation is that, the proposed model can be tested by analogy, considering also the fraud detection problem in credit cards, where the amount of training patterns used are high.

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Predictive Analysis of Employee Loyalty: A Comparative Study Using Logistic Regression Model and Artificial Neural Network

Journal of the Indonesian Mathematical Society ◽

10.22342/jims.25.3.825.325-335 ◽

2019 ◽

Vol 25 (3) ◽

pp. 325-335

Author(s):

Maria Zefanya Sampe ◽

Eko Ariawan ◽

I Wayan Ariawan

Keyword(s):

Logistic Regression ◽

Employee Turnover ◽

Mean Squared Error ◽

Working Hours ◽

Retention Rates ◽

Ann Model ◽

Employee Loyalty ◽

High Turnover ◽

Kolmogorov Smirnov ◽

Artificial Neural

Employee turnover is a common issue in any company. A high turnover phenomenon becomes a big problem that will certainly affect the performance of the company. Therefore, measuring employee turnover can be helpful to employers to improve employee retention rates and give them a head start on turnover. A study to analyze for employee loyalty has been carried out by using Logistic Regression (LR) and Artificial Neural Networks (ANN) model. Response variables such as satisfaction level, number of projects, average monthly working hours, employment period, working accident, promotion in the last 5 years, department, and salary level are used to model the employee turnover. Parameters such as accuracy, precision, sensitivity, Kolmogorov-Smirnov statistic, and Mean Squared Error (MSE) are used to compare both models.

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Stock Price Prediction using Artificial Neural Model: An Application of Big Data

ICST Transactions on Scalable Information Systems ◽

10.4108/eai.19-12-2018.156085 ◽

2018 ◽

Vol 0 (0) ◽

pp. 156085 ◽

Cited By ~ 9

Author(s):

Malav Shastri ◽

Sudipta Roy ◽

Mamta Mittal

Keyword(s):

Big Data ◽

Stock Price ◽

Neural Model ◽

Stock Price Prediction ◽

Price Prediction ◽

Artificial Neural

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Monitoring Water Resources through a Bayesian Optimization-based Approach using Multiple Surface Vehicles: The Ypacarai Lake Case Study

10.36227/techrxiv.14267546 ◽

2021 ◽

Author(s):

Federico Peralta Samaniego ◽

Sergio Toral Marín ◽

Daniel Gutierrez Reina

Keyword(s):

Water Quality ◽

Mean Squared Error ◽

Bayesian Optimization ◽

Water Quality Parameter ◽

Optimization Approach ◽

Decision Strategy ◽

Sequential Decision ◽

Partition System ◽

Autonomous Surface Vehicle ◽

Voronoi Partition

<div>Bayesian optimization is a popular sequential decision strategy that can be used for environmental monitoring. In this work, we propose an efficient multi-Autonomous Surface Vehicle system capable of monitoring the Ypacarai Lake (San Bernardino, Paraguay) (60 km2) using the Bayesian optimization approach with a Voronoi Partition system. The system manages to quickly approximate the real unknown distribution map of a water quality parameter using Gaussian Processes as surrogate models. Furthermore, to select new water quality measurement locations, an acquisition function adapted to vehicle energy constraints is used. Moreover, a Voronoi Partition system helps to distributing the workload with all the available vehicles, so that robustness and scalability is assured. For evaluation purposes, we use both the mean squared error and computational efficiency. The results showed that our method manages to efficiently monitor the Ypacarai Lake, and also provides confident approximate models of water quality parameters. It has been observed that, for every vehicle, the resulting surrogate model improves by 38%.</div>

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Development of a Computationally Intelligent Model to Estimate Oil Formation Volume Factor

10.4043/31312-ms ◽

2021 ◽

Author(s):

Mohammad Rasheed Khan ◽

Shams Kalam ◽

Rizwan Ahmed Khan

Keyword(s):

Crude Oil ◽

Mean Squared Error ◽

Evaluation Studies ◽

Coefficient Of Determination ◽

Volume Factor ◽

Oil Formation Volume Factor ◽

Artificial Neural ◽

Error Metric ◽

Intelligent Model ◽

Oil Formation

Abstract This investigation presents a powerful predictive model to determine crude oil formation volume factor (FVF) using state-of-the-art computational intelligence (CI) techniques. FVF is a vital pressure-volume-temperature (PVT) parameter used to characterize hydrocarbon systems and is pivotal to reserve evaluation studies and reservoir engineering calculations. Ideally, FVF is measured at the laboratory scale; however, prognostic tools to evaluate this parameter can aid in optimizing time and cost estimates. The database utilized in this study is obtained from open literature and covers statistics of crude oils of Pakistan, Iran, UAE, and Malaysia. Resultantly, this allows to move step forward towards the creation of a generalized model. Multiple CI algorithms are considered, including Artificial Neural Networks (ANN) and Artificial Neural Fuzzy Inference Systems (ANFIS). Models for CI are developed utilizing an optimization strategy for various parameters/hyper-parameters of the respective algorithms. Unique permutations and combinations for the number of perceptron and their resident layers is investigated to reach a solution that provides the most optimum output. These intelligent models are produced as a function of the parameters intrinsically affecting FVF; reservoir temperature, solution GOR, gas specific gravity, and crude oil API gravity. Comparative analysis of various CI models is performed using visualization/statistical analysis and the best model pointed out. Finally, the mathematical equation extraction to determine FVF is accomplished with the respective weights and bias for the model presented. Graphical analysis using scatter plots with a coefficient of determination (R2) illustrates that ANN equation produces the most accurate predictions for oil FVF with R2 in excess of 0.96. Moreover, during this study an error metric is developed comprising of multiple analysis parameters; Average Absolute Error (AAE), Root Mean Squared Error (RMSE), correlation coefficient (R). All models investigated are tested on an unseen dataset to prevent the development of a biased model. Performance of the established CI models are gauged based on this error metric, which demonstrates that ANN outperforms the other models with error within 2% of the measured PVT values. A computationally derived intelligent model proves to provide the strongest predictive capabilities as it maps complex non-linear interactions between various input parameters leading to FVF.

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