scholarly journals Corrosion Predictive Model in Hot-Dip Galvanized Steel Buried in Soil

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Lorena-De Arriba-Rodríguez ◽  
Francisco Ortega-Fernández ◽  
Joaquín M. Villanueva-Balsera ◽  
Vicente Rodríguez-Montequín
Keyword(s):  
Predictive Model ◽  
Structural Engineering ◽  
Quantitative Methods ◽  
A Priori ◽  
Metal Structure ◽  
Galvanized Steel ◽  
Standardized Residuals ◽  
Useful Life ◽  
Input Variables ◽  
Objective Variable

Corrosion is one of the main concerns in the field of structural engineering due to its effect on steel buried in soil. Currently, there is no clearly established method that allows its calculation with precision and ensures the durability of this type of structures. Qualitative methods are commonly used rather than quantitative methods. The objective of this research is the development of a multivariate quantitative predictive model for estimating the loss of thickness that will occur in buried hot-dip galvanized steel as a function of time. The technique used in the modelling is the Adaptive Regression of Multivariate Splines (MARS). The main drawback of this kind of studies is the lack of data since it is not possible to have a priori the corrosive behaviour that the buried material will have as a function of time. To solve this issue, a solid and reliable database was built from the analysis and treatment of the existing literature and with the results obtained from a predictive model to estimate the thickness loss of ungalvanized steel. The input variables of the model are 5 characteristics of the soil, the useful life of the structure, and the loss of corroded ungalvanized steel in the soil. This last data is the output variable of another previous predictive model to estimate the loss of thickness of bare steel in a soil. The objective variable of the model is the loss of thickness that hot-dip galvanized steel will experience buried in the ground and expressed in g/m2. To evaluate the performance and applicability of the proposed model, the statistical metrics RMSE, R2, MAE, and RAE and the graphs of standardized residuals were used. The results indicated that the model offers a very high prediction performance. Specifically, the mean square error was 290.6 g/m2 (range of the objective variable is from 51.787 g/m2 to 5950.5 g/m2), R2 was 0.96, and from a relative error of 0.14, the success of the estimate was 100%. Therefore, the use of the proposed predictive model optimizes the relationship between the amount of hot-dip galvanized steel and the useful life of the buried metal structure.

Development of Heat Exchanger Fouling Model and Preventive Maintenance Diagnostic Tool

2007 ◽  
Vol 2 (2) ◽  
Author(s):  
H. Zabiri ◽  
V. R. Radhakrishnan ◽  
M. Ramasamy ◽  
N. M. Ramli ◽  
V. Do Thanh ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Heat Exchanger ◽  
Predictive Model ◽  
Diagnostic Tool ◽  
Preventive Maintenance ◽  
Heat Exchangers ◽  
Waste Heat ◽  
A Priori ◽  
Transfer Efficiency ◽  
Heat Transfer Efficiency

The Crude Preheat Train (CPT) is a set of large heat exchangers which recover the waste heat from product streams back to preheat the crude oil. The overall heat transfer coefficient in these heat exchangers may be significantly reduced due to fouling. One of the major impacts of fouling in CPT operation is the reduced heat transfer efficiency. The objective of this paper is to develop a predictive model using statistical methods which can a priori predict the rate of the fouling and the decrease in heat transfer efficiency in a heat exchanger in a crude preheat train. This predictive model will then be integrated into a preventive maintenance diagnostic tool to plan the cleaning of the heat exchanger to remove the fouling and bring back the heat exchanger efficiency to their peak values. The fouling model was developed using historical plant operating data and is based on Neural Network. Results show that the predictive model is able to predict the shell and tube outlet temperatures with excellent accuracy, where the Root Mean Square Error (RMSE) obtained is less than 1%, correlation coefficient R2 of approximately 0.98 and Correct Directional Change (CDC) values of more than 90%. A preliminary case study shows promising indication that the predictive model may be integrated into a preventive maintenance scheduling for the heat exchanger cleaning.

Economical design for long-span soil – metal structures

1996 ◽  
Vol 23 (4) ◽  
pp. 838-849 ◽  
Author(s):  
Hesham Mohammed ◽  
John B. Kennedy
Keyword(s):  
Dimensional Analysis ◽  
Structural Engineering ◽  
Three Dimensional ◽  
Metal Structure ◽  
Reinforced Soil ◽  
Soil System ◽  
Metal Structures ◽  
Long Span ◽  
Economical Design ◽  
Soil Metal

Soil – metal structures consisting of metal conduits covered with soil have been used extensively for short-span bridges. Recently, some designers ventured into utilizing them for longer spans with shallow soil cover which has led to some failures. Long-span soil – metal structures are often designed with transverse stiffeners attached to the metal structure. Another approach is the use of a reinforced-soil system in which the surrounding soil is reinforced and the metal conduit is tied into the soil. In this paper, a three-dimensional analysis of long-span soil – metal structures is carried out using these two approaches. The analysis is verified and substantiated by results from laboratory models. The structural responses from the two designs show that the latter design approach leads to a more economical structure. A design example based on the Cheese Factory Bridge built in Ontario in 1984 is presented. Key words: bridges, design, long span, reinforced soil, soil – metal structures, structural engineering, three-dimensional analysis.

Comparing a Machine Learning Predictive Model with Federal Transit Administration (FTA)’s Default Useful Life Benchmark to Predict Replacement Costs for Revenue Vehicles

2020 ◽  
Vol 2674 (2) ◽  
pp. 181-190
Author(s):  
Dilip Mistry ◽  
Jill Hough
Keyword(s):  
Machine Learning ◽  
Service Life ◽  
Predictive Model ◽  
Learning Algorithm ◽  
The State ◽  
Decision Makers ◽  
Machine Learning Algorithm ◽  
Useful Life ◽  
Transit Agencies ◽  
The U.S

A predictive model is developed that uses a machine learning algorithm to predict the service life of transit vehicles and calculates backlog and yearly replacement costs to achieve and maintain transit vehicles in a state of good repair. The model is applied to data from the State of Oklahoma. The vehicle service lives predicted by the machine learning predictive model (MLPM) are compared with the default useful life benchmark (ULB) of the U.S. Federal Transit Administration (FTA). The model shows that the service life predicted by the MLPM provides relatively more realistic predictions of replacement costs of revenue vehicles than the predictions generated using FTA’s default ULB. The MLPM will help Oklahoma’s transit agencies facilitate the state of good repair analysis of their transit vehicles and guide decision makers when investing in rehabilitation and replacement needs. The paper demonstrates that it is advantageous to use a MLPM to predict the service life of revenue vehicles in place of the FTA’s default ULB.

Flow Regime Identification of Horizontal Two Phase Refrigerant R-134a Flow Using Neural Networks

2013 ◽  
Author(s):  
Paul J. Kreitzer ◽  
Michael Hanchak ◽  
Larry Byrd
Keyword(s):  
Neural Networks ◽  
Flow Regime ◽  
A Priori ◽  
Horizontal Tube ◽  
Flow Regimes ◽  
Separate Flow ◽  
Two Phase ◽  
Flow Regime Identification ◽  
Input Variables ◽  
R 134A

Flow regime Identification is an integral aspect of modeling two phase flows as most pressure drop and heat transfer correlations rely on a priori knowledge of the flow regime for accurate system predictions. In the current research, two phase R-134a flow is studied in a 7mm adiabatic horizontal tube over a mass flux range of 100–400 kg/m2s between 550–750 kPa. Electric Capacitance Tomography results for 196 test points were analyzed using statistical methods and neural networks. This data provided repeatable normalized permittivity ratio signatures based on the flow distributions. The first four temporal moments from the mean scaled permittivity data were utilized as input variables. Results showed that only 80 percent of flow regimes could be correctly identified using seven flow regime classifications. However reducing to five more commonly used regimes resulted in an improvement to 99 percent of the flow regimes correctly identified. Both methods of neural network training resulted in errors that were off by mostly one flow regime classification. Further analysis shows that transition cases can oscillate between two separate flow regimes at the same time.

Ensemble Prognostics With Degradation-Dependent Weights: Prediction of Remaining Useful Life for Aircraft Engines

2017 ◽  
Author(s):  
Zhixiong Li ◽  
Dazhong Wu ◽  
Chao Hu ◽  
Janis Terpenny ◽  
Sheng Shen
Keyword(s):  
Predictive Model ◽  
Ensemble Learning ◽  
Learning Algorithm ◽  
Remaining Useful Life ◽  
Weighted Sum ◽  
Aircraft Engines ◽  
Data Set ◽  
Ensemble Learning Algorithm ◽  
Useful Life ◽  
Mechanical Components

The objective of this research is to introduce a new ensemble prognostics method with degradation-dependent weights. Specifically, this method assigns an optimized, degradation-dependent weight to each learner (i.e., learning algorithm) such that the weighted sum of the prediction results from all the learners predicts the RUL of mechanical components with better accuracy. The ensemble prognostic algorithm is demonstrated using a data set collected from an engine simulator. Analysis results show that the predictive model trained by the ensemble learning algorithm outperform the existing methods.

Modelling Tyre-Road Noise with Data Mining Techniques

2015 ◽  
Vol 40 (4) ◽  
pp. 547-560 ◽  
Author(s):  
Elisabete Freitas ◽  
Joaquim Tinoco ◽  
Francisco Soares ◽  
Jocilene Costa ◽  
Paulo Cortez ◽  
...  
Keyword(s):  
Data Mining ◽  
Predictive Model ◽  
Early Stage ◽  
Aggregate Size ◽  
Noise Prediction ◽  
Road Noise ◽  
Predictive Capacity ◽  
Data Mining Approach ◽  
Relevant Variables ◽  
Input Variables

Abstract The research aimed to establish tyre-road noise models by using a Data Mining approach that allowed to build a predictive model and assess the importance of the tested input variables. The data modelling took into account three learning algorithms and three metrics to define the best predictive model. The variables tested included basic properties of pavement surfaces, macrotexture, megatexture, and unevenness and, for the first time, damping. Also, the importance of those variables was measured by using a sensitivity analysis procedure. Two types of models were set: one with basic variables and another with complex variables, such as megatexture and damping, all as a function of vehicles speed. More detailed models were additionally set by the speed level. As a result, several models with very good tyre-road noise predictive capacity were achieved. The most relevant variables were Speed, Temperature, Aggregate size, Mean Profile Depth, and Damping, which had the highest importance, even though influenced by speed. Megatexture and IRI had the lowest importance. The applicability of the models developed in this work is relevant for trucks tyre-noise prediction, represented by the AVON V 4 test tyre, at the early stage of road pavements use. Therefore, the obtained models are highly useful for the design of pavements and for noise prediction by road authorities and contractors.

scholarly journals Classification of the state of the dynamic system, which is functioned by which is described by vector autoresis

2019 ◽  
Vol 4 (123) ◽  
pp. 131-154
Author(s):  
Oleksandr Pavlovych Sarychev
Keyword(s):  
Dynamic System ◽  
Power Plants ◽  
A Priori ◽  
Technical Condition ◽  
Regression Equations ◽  
Unknown Parameters ◽  
Term Operation ◽  
Space Objects ◽  
Input Variables

Within the framework of the article, the problem of statistical classification of states of a dynamic system is solved, which can be in two classes of states, in each of which its operation is described by its own system of autoregressive equations with a priori unknown parameters. It is assumed that the following conditions are fulfilled: a) two classes of states are described by the same sets of observed input and output variables; b) the output variables, both in the first and in the second class, are determined by different sets of regressors (input variables); c) the models of functioning in the first and second classes are different both in terms of coefficients and in the structure of autoregressive models; d) the covariance matrices of random variables in the functioning models and the observation models for the first and second classes are different. The rule of classification is constructed and its properties are investigated.The experience of successfully solving problems of detecting changes in the properties of dynamic systems based on regression equations in the work, where an approach to constructing mathematical models for monitoring the technical condition of power and power plants in long-term operation was proposed, shows the feasibility of applying this approach to solving problems of controlling the operation of rocket-space objects technology.The problem of classifying states of a dynamic system, which can be in two classes of states, is considered. The functioning of the system in classes is described by various systems of autoregressive equations. The rule of classification is constructed and its properties are investigated.

Foods and Nutritional Characteristics Associated With Addictive-Like Eating

2021 ◽  
pp. 003329412110141
Author(s):  
Kirstie M. Herb Neff ◽  
Angela Fay ◽  
Karen K. Saules
Keyword(s):  
Quantitative Methods ◽  
Qualitative Data ◽  
Predictive Power ◽  
Food Addiction ◽  
A Priori ◽  
Food Additives ◽  
Mixed Methods Study ◽  
High Sodium ◽  
Nutritional Characteristics ◽  
The Relationship

Emerging literature is exploring the contribution of specific nutritional characteristics and food additives to the development of addictive-like eating, implicating highly processed foods and those high in fat and sugar in its pathophysiology. To our knowledge, no mixed methods study has yet aimed to investigate the relationship between food characteristics and addictive-like eating. Towards this end, we applied an a priori classification scheme to open-ended answers to enable us to use quantitative methods to analyze qualitative data. A sample of individuals who endorsed self-perceived “food addiction” (N = 182; 50% female; Mage = 34.1) reported the foods to which they believed they were “addicted.” We classified these foods according to their levels of fat, carbohydrates, sugar, and sodium, and evaluated their predictive power on addictive-like eating. Pizza, chocolate, hamburgers, and pasta respectively, were the most reported food items to which participants felt they were addicted. Addictive-like eating was significantly predicted by endorsement of “addiction” to high-sodium foods. In contrast, “addiction” to high-sugar foods negatively predicted addictive-like eating symptoms. Findings support an association between highly processed and high-sodium foods with addictive-like eating behavior among humans, consistent in large part with prior human and animal literature. Results also suggest that people are readily able to report on their experiences of addiction to foods; specifically, they can freely endorse the experience of addictive-like eating and offer experiences of addictive foods that are largely consistent with theory and the literature.

scholarly journals Electrochemical Evaluation of Galvanized Steel and AISI 1018 as Reinforcement in a Soil Type MH

2020 ◽  
Vol 5 (3) ◽  
pp. 259-263 ◽  
Author(s):  
Miguel Angel Baltazar-Zamora ◽  
Laura Landa-Ruiz ◽  
Yazmin Rivera ◽  
René Croche
Keyword(s):  
Soil Classification ◽  
Galvanized Steel ◽  
High Plasticity ◽  
Cell Potential ◽  
Linear Polarization Resistance ◽  
Fine Soil ◽  
Corrosion Evaluation ◽  
Steel Bars ◽  
Useful Life ◽  
Electrochemical Evaluation

This work presents the electrochemical evaluation of bars of Galvanized Steel and AISI 1018 with 3/8” and ½” of diameter, this bars are commonly used for the construction of elements based on Soils Mechanically Reinforced (SMR), the bars were buried in a fine soil predominant in the region of Xalapa City, Ver., México, soil classified in the USCS (Unified Soil Classification System) as a high plasticity silt (MH). Corrosion evaluation was conducted by monitoring the corrosion potential Ecorr and corrosion rate, Icorr, using techniques half-cell potential according to the standard ASTM C-876-15 and Linear Polarization Resistance (LPR), respectively. The experimental setup simulates the real conditions when the steel is used as reinforcement in structures of SMR, where they remain buried throughout the useful life of the structure. The results of the first 110 days of exposure show that the Galvanized Steel bars have a better corrosion performance compared to the AISI 1018 steel regardless of their diameter.

scholarly journals Improvement Of Confidence And Motivation Using Online Metacognition Tool

2012 ◽  
Vol 3 (1) ◽  
pp. 53-66
Author(s):  
Quamrul H. Mazumder ◽  
Mary Jo Finney
Keyword(s):  
Academic Performance ◽  
Academic Success ◽  
Learning Outcomes ◽  
Quantitative Methods ◽  
Engineering Students ◽  
Communication Apprehension ◽  
Interactive Technology ◽  
Interactive Software ◽  
Qualitative And Quantitative Methods ◽  
Input Variables

The use of technology such as laptop computers in the classroom has long been recognized as destructive behavior since it diverts a student’s attention from course topics. However, it is conceivable that every student will be using some form of technology in the near future. Determining the effects of interactive software on students’ learning outcomes can have a profound effect on engineering education. The ultimate aim of this research is to transform students into active learners who are able to better comprehend, are less distracted, and can achieve higher academic performance. In this study, first year engineering students used online metacognition software while interactively participating in the classroom. Both qualitative and quantitative methods using the pre- and post-test experimental designs as well as a debriefing questionnaire were utilized. The academic achievement of students’ through the integration of interactive technology was the output variable, while the input variables were divided into four categories: students’ understanding of the concepts, confidence level, apprehension level, and motivation. In addition, this study also examined the amount of class participation to measure students’ communication apprehension and its correlation to academic performance. In order to improve students' learning outcomes using metacognitive strategies, it was discovered that the use of interactive technology followed by group discussions and class assignments greatly enhanced students' comprehension of scientific facts and their ability to explain them. In addition, the current study showed that engineering students' communication apprehension was also reduced resulting in improvement in confidence and motivation towards academic success. 

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