Application of the Prediction Model "SCHALL 03" for Railway Noise Calculation in Serbia

2013 ◽  
Vol 430 ◽  
pp. 237-243 ◽  
Author(s):  
Momir Praščevič ◽  
Aleksandar Gajicki ◽  
Darko Mihajlov ◽  
Nenad Živković ◽  
Ljiljana Zivkovic
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
National Level ◽  
Rolling Stock ◽  
Railway Network ◽  
Local Conditions ◽  
Railway Noise ◽  
Technological Characteristics ◽  
Accuracy And Precision ◽  
Railway Infrastructure

Application of the prediction models for railway noise indicators calculation, which was already developed by other countries, represents a major challenge in Serbia. Prediction model "Schall 03" was developed in line with technical and technological characteristics of the rolling stock and infrastructure of German Railways. Prior to its application at the national level, due to different technical-technological characteristics of railway stock and railway infrastructure it is necessary to perform its validation and, depending on the needs, the calibration in accordance with local conditions. This will assure accuracy and precision of the calculations of noise indicators, as well as confidence in the results obtained by prediction model "Schall 03". This paper presents the analysis of the possibilities to apply German prediction model "Schall 03" on the Serbian railway network, more precisely railway section from Belgrade to Romanian border. Calculated values of noise indicators were compared with the results of measurements of noise indicators within measuring interval, which correspond to the referent time intervals for different day periods.

scholarly journals A Novel Prediction Model of Strength of Paste Backfill Prepared from Waste-Unclassified Tailings

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Haiyong Cheng ◽  
Shunchuan Wu ◽  
Xiaoqiang Zhang ◽  
Junhong Li
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Orthogonal Design ◽  
Influential Factors ◽  
Waste Rock ◽  
Strength Prediction ◽  
Strength Development ◽  
Local Conditions ◽  
Unclassified Tailings ◽  
Paste Backfill

Paste backfilling is an important support for the development of green mines and deep mining. It can effectively reduce a series of risks of underground goaf and surface tailings ponds. Reasonable strength of backfill is an effective guarantee for controlling ground stress and realizing safe mining function. Under the combination of complex materials and local conditions, ensuring the optimal design and effective proportion for paste backfill strength is the bottleneck problem that restricts the safety, economy, and efficiency of filling mining. The strength developing trend of paste backfilling prepared from waste rock and unclassified tailings has been studied. Different levels of cement contents, tailings-waste ratios, and slurry concentrations were investigated through orthogonal design to obtain the relationship between the UCS and the multi-influential factors. Combined with the experimental results and the previous strength prediction models, the waste rock-unclassified tailings paste strength prediction model was proposed. Introducing the water-cement ratio, the cement-tailings ratio, the amount of cement, and the packing density that characterizing the overall gradation of unclassified tailings and waste rock, as well as the curing time, a strength prediction model of multifactors was developed. Moreover, the microscopic structure of the paste prepared from waste-unclassified tailings was analyzed with an Environment Scanning Electron Microscope (ESEM), and the influence mechanism was ascertained. The weight coefficient of strength development is carded in this paper, and the strength model of unclassified tailings-waste paste considering five factors is obtained, which is of great significance to guide the mining engineering.

scholarly journals Development of Roughness Prediction Models for Laos National Road Network

CivilEng ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 158-173
Author(s):  
Mohamed Gharieb ◽  
Takafumi Nishikawa
Keyword(s):  
Prediction Model ◽  
Road Network ◽  
Prediction Models ◽  
Parameter Analysis ◽  
Local Conditions ◽  
Pavement Condition ◽  
Analysis Technique ◽  
Roughness Prediction ◽  
National Road ◽  
Development And Management

The International Roughness Index (IRI) has been accepted globally as an essential indicator for assessing pavement condition. The Laos Road Management System (RMS) utilizes a default Highway Development and Management (HDM-4) IRI prediction model. However, developed IRI values have shown the need to calibrate the IRI prediction model. Data records are not fully available for Laos yet, making it difficult to calibrate IRI for the local conditions. This paper aims to develop an IRI prediction model for the National Road Network (NRN) based on the available Laos RMS database. The Multiple Linear Regression (MLR) analysis technique was applied to develop two new IRI prediction models for Double Bituminous Surface Treatment (DBST) and Asphalt Concrete (AC) pavement sections. The final database consisted of 83 sections with 269 observations over a 1850 km length of DBST NRN and 29 sections with 122 observations over a 718 km length of AC NRN. The proposed models predict IRI as a function of pavement age and Cumulative Equivalent Single-Axle Load (CESAL). The model’s parameter analysis confirmed their significance, and R2 values were 0.89 and 0.84 for DBST and AC models, respectively. It can be concluded that the developed models can serve as a useful tool for engineers maintaining paved NRN.

scholarly journals Analysis of Policies of Railway Operators Using SWOT Criteria and the SIMUS Method: A Case for the Bulgarian Railway Network

2021 ◽  
Vol 13 (12) ◽  
pp. 6948
Author(s):  
Svetla Stoilova ◽  
Nolberto Munier
Keyword(s):  
Status Quo ◽  
Urban Systems ◽  
Rolling Stock ◽  
Railway Network ◽  
Second Stage ◽  
Railway Infrastructure ◽  
The Status ◽  
Interactive Modelling ◽  
Two Stages ◽  
Railway Operation

This study is useful for railway operators as it enables them to verify their decisions against the results of the application of the techniques of strategic planning and multi-criteria analysis. It gives railway stakeholders concise, objective and unbiased information so that they can then make decisions and also allows them to determine the strengths and sensitivity, of the best solution found. This paper presents a methodology for the assessment of the policies of railway operators using Strengths–Weakness–Opportunities–Threats (SWOT) criteria and the Sequential Interactive Modelling for Urban Systems (SIMUS) method. The methodology of the research consists of two stages. In the first stage, the alternatives of the policies for the railway operator are formulated; the criteria in the SWOT group are defined; and the values of the criteria are determined for each of the alternatives. In the second stage, the SIMUS method is applied to rank the alternatives and assess the criteria in the SWOT groups. The criteria are interpreted as objectives and linear optimizations are performed. A comparison between the desired values for each objective of the SWOT criteria and the optimum values of the objective functions obtained by SIMUS was made. The methodology was applied to the Bulgarian railway network. Three policies for railway operation were studied. The total number of 17 railway policies criteria in the SWOT group were defined and assessed—three strengths criteria, seven weaknesses criteria, three opportunities criteria and four threats criteria. The results indicated that the best strategy is A3 (some reconstruction of the railway infrastructure and new rolling stock on some lines), with the highest score of 3.76, followed by A2 (new rolling stock on some lines), with a score of 2.71. The status-quo strategy (A1) has a very low score of 0.43, that the current situation or status-quo cannot be supported. The weights of both strengths and opportunities are both of the same importance with a weight of 0.180. It was found out that the clusters Weakness and Threats are dominant with weights of 0.4 and 0.24 respectively. The results show that the weights are all practically the same, about 0.06, and therefore, no discrimination by importance is possible. The methodology makes it possible to consider the alternatives simultaneously, and in this way, the results will reflect the effect of one criterion on all others, and permit us to quantify the differences between expected and real results.

Bioactivity Prediction Based on Matched Molecular Pair and Matched Molecular Series Methods

2020 ◽  
Vol 26 (33) ◽  
pp. 4195-4205
Author(s):  
Xiaoyu Ding ◽  
Chen Cui ◽  
Dingyan Wang ◽  
Jihui Zhao ◽  
Mingyue Zheng ◽  
...  
Keyword(s):  
Prediction Model ◽  
Large Scale ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Lead Optimization ◽  
Consensus Method ◽  
Molecular Pair ◽  
Bioactivity Prediction ◽  
Compound Synthesis ◽  
Consensus Modeling

Background: Enhancing a compound’s biological activity is the central task for lead optimization in small molecules drug discovery. However, it is laborious to perform many iterative rounds of compound synthesis and bioactivity tests. To address the issue, it is highly demanding to develop high quality in silico bioactivity prediction approaches, to prioritize such more active compound derivatives and reduce the trial-and-error process. Methods: Two kinds of bioactivity prediction models based on a large-scale structure-activity relationship (SAR) database were constructed. The first one is based on the similarity of substituents and realized by matched molecular pair analysis, including SA, SA_BR, SR, and SR_BR. The second one is based on SAR transferability and realized by matched molecular series analysis, including Single MMS pair, Full MMS series, and Multi single MMS pairs. Moreover, we also defined the application domain of models by using the distance-based threshold. Results: Among seven individual models, Multi single MMS pairs bioactivity prediction model showed the best performance (R2 = 0.828, MAE = 0.406, RMSE = 0.591), and the baseline model (SA) produced the most lower prediction accuracy (R2 = 0.798, MAE = 0.446, RMSE = 0.637). The predictive accuracy could further be improved by consensus modeling (R2 = 0.842, MAE = 0.397 and RMSE = 0.563). Conclusion: An accurate prediction model for bioactivity was built with a consensus method, which was superior to all individual models. Our model should be a valuable tool for lead optimization.

Coordination of technology of railway operation of the Republic of Tajikistan and organization of railways in international traffic

2018 ◽  
Vol 77 (4) ◽  
pp. 211-217 ◽  
Author(s):  
P. N. Pulatov
Keyword(s):  
Rolling Stock ◽  
Railway Transport ◽  
Transport Services ◽  
Railway Infrastructure ◽  
External Conditions ◽  
The Republic ◽  
Railway Operation ◽  
Required Quality

Current geopolitical and economic conditions for the functioning of railway transport in most post-Soviet states are such that it is extremely difficult to provide required quality of transport services and break-even operations at high expenses for maintaining the railway infrastructure and rolling stock. Dynamics of transportation of the Tajik Railway (TSR) is shown, which displays that most of its sections are classified as low-intensity ones. The paper proposes methodical principles, setting and qualitative analysis of the task of rationalization of operational work and organization of car flows for international transportation, taking into account the specifics of the Tajik Railway. There is a problem of complex maintenance of the efficiency of operational work in modern conditions based on the synthesis of the tasks of self-management (rational internal operational technology of the Tajik Railway) and coordination tasks (technological interaction with railway administrations of other states). Author substantiated the necessity of solving this problem. Proposed classification of technological restrictions and controlled variables in the performance of transport takes into account methods for changing external conditions for the functioning of the railway landfill and methods for increasing internal efficiency of its operation. The search for the solution of the problem involves direct search of variants along its ordered set with clipping of groups of variants that do not correspond to constraints, with the subsequent finding of compromise control over a set of effective alternatives.

Fire modelling in Tasmanian buttongrass moorlands. III. Dead fuel moisture

2001 ◽  
Vol 10 (2) ◽  
pp. 241 ◽  
Author(s):  
Jon B. Marsden-Smedley ◽  
Wendy R. Catchpole
Keyword(s):  
Prediction Model ◽  
Regression Model ◽  
Fire Management ◽  
Prediction Models ◽  
Dew Point ◽  
Seasonal Effects ◽  
Experimental Program ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Fire Modelling

An experimental program was carried out in Tasmanian buttongrass moorlands to develop fire behaviour prediction models for improving fire management. This paper describes the results of the fuel moisture modelling section of this project. A range of previously developed fuel moisture prediction models are examined and three empirical dead fuel moisture prediction models are developed. McArthur’s grassland fuel moisture model gave equally good predictions as a linear regression model using humidity and dew-point temperature. The regression model was preferred as a prediction model as it is inherently more robust. A prediction model based on hazard sticks was found to have strong seasonal effects which need further investigation before hazard sticks can be used operationally.

scholarly journals Experimental Strain Measurement Approach Using Fiber Bragg Grating Sensors for Monitoring of Railway Switches and Crossings

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3639
Author(s):  
Abdelfateh Kerrouche ◽  
Taoufik Najeh ◽  
Pablo Jaen-Sola
Keyword(s):  
Fiber Bragg Grating ◽  
Experimental Tests ◽  
Cost Effective ◽  
Measuring Method ◽  
Bragg Grating ◽  
Railway Network ◽  
System Failures ◽  
Railway Infrastructure ◽  
Train Speed ◽  
And Control

Railway infrastructure plays a major role in providing the most cost-effective way to transport freight and passengers. The increase in train speed, traffic growth, heavier axles, and harsh environments make railway assets susceptible to degradation and failure. Railway switches and crossings (S&C) are a key element in any railway network, providing flexible traffic for trains to switch between tracks (through or turnout direction). S&C systems have complex structures, with many components, such as crossing parts, frogs, switchblades, and point machines. Many technologies (e.g., electrical, mechanical, and electronic devices) are used to operate and control S&C. These S&C systems are subject to failures and malfunctions that can cause delays, traffic disruptions, and even deadly accidents. Suitable field-based monitoring techniques to deal with fault detection in railway S&C systems are sought after. Wear is the major cause of S&C system failures. A novel measuring method to monitor excessive wear on the frog, as part of S&C, based on fiber Bragg grating (FBG) optical fiber sensors, is discussed in this paper. The developed solution is based on FBG sensors measuring the strain profile of the frog of S&C to determine wear size. A numerical model of a 3D prototype was developed through the finite element method, to define loading testing conditions, as well as for comparison with experimental tests. The sensors were examined under periodic and controlled loading tests. Results of this pilot study, based on simulation and laboratory tests, have shown a correlation for the static load. It was shown that the results of the experimental and the numerical studies were in good agreement.

scholarly journals A Genetic Algorithm Optimized RNN-LSTM Model for Remaining Useful Life Prediction of Turbofan Engine

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 285
Author(s):  
Kwok Tai Chui ◽  
Brij B. Gupta ◽  
Pandian Vasant
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Prediction Model ◽  
Prediction Models ◽  
Remaining Useful Life ◽  
Prediction Algorithm ◽  
Short Term ◽  
Turbofan Engine ◽  
Term Prediction ◽  
Useful Life

Understanding the remaining useful life (RUL) of equipment is crucial for optimal predictive maintenance (PdM). This addresses the issues of equipment downtime and unnecessary maintenance checks in run-to-failure maintenance and preventive maintenance. Both feature extraction and prediction algorithm have played crucial roles on the performance of RUL prediction models. A benchmark dataset, namely Turbofan Engine Degradation Simulation Dataset, was selected for performance analysis and evaluation. The proposal of the combination of complete ensemble empirical mode decomposition and wavelet packet transform for feature extraction could reduce the average root-mean-square error (RMSE) by 5.14–27.15% compared with six approaches. When it comes to the prediction algorithm, the results of the RUL prediction model could be that the equipment needs to be repaired or replaced within a shorter or a longer period of time. Incorporating this characteristic could enhance the performance of the RUL prediction model. In this paper, we have proposed the RUL prediction algorithm in combination with recurrent neural network (RNN) and long short-term memory (LSTM). The former takes the advantages of short-term prediction whereas the latter manages better in long-term prediction. The weights to combine RNN and LSTM were designed by non-dominated sorting genetic algorithm II (NSGA-II). It achieved average RMSE of 17.2. It improved the RMSE by 6.07–14.72% compared with baseline models, stand-alone RNN, and stand-alone LSTM. Compared with existing works, the RMSE improvement by proposed work is 12.95–39.32%.

scholarly journals The Role of Board Independence and Ownership Structure in Improving the Efficacy of Corporate Financial Distress Prediction Model Evidence from India

2021 ◽  
Vol 14 (7) ◽  
pp. 333
Author(s):  
Shilpa H. Shetty ◽  
Theresa Nithila Vincent
Keyword(s):  
Prediction Model ◽  
Ownership Structure ◽  
Financial Distress ◽  
Prediction Models ◽  
Receiver Operating Curve ◽  
Financial Measures ◽  
Financial Variables ◽  
Financial Distress Prediction ◽  
Distress Prediction

The study aimed to investigate the role of non-financial measures in predicting corporate financial distress in the Indian industrial sector. The proportion of independent directors on the board and the proportion of the promoters’ share in the ownership structure of the business were the non-financial measures that were analysed, along with ten financial measures. For this, sample data consisted of 82 companies that had filed for bankruptcy under the Insolvency and Bankruptcy Code (IBC). An equal number of matching financially sound companies also constituted the sample. Therefore, the total sample size was 164 companies. Data for five years immediately preceding the bankruptcy filing was collected for the sample companies. The data of 120 companies evenly drawn from the two groups of companies were used for developing the model and the remaining data were used for validating the developed model. Two binary logistic regression models were developed, M1 and M2, where M1 was formulated with both financial and non-financial variables, and M2 only had financial variables as predictors. The diagnostic ability of the model was tested with the aid of the receiver operating curve (ROC), area under the curve (AUC), sensitivity, specificity and annual accuracy. The results of the study show that inclusion of the two non-financial variables improved the efficacy of the financial distress prediction model. This study made a unique attempt to provide empirical evidence on the role played by non-financial variables in improving the efficiency of corporate distress prediction models.

Predicting Change Prone Classes in Open Source Software

2018 ◽  
Vol 8 (4) ◽  
pp. 1-23 ◽  
Author(s):  
Deepa Godara ◽  
Amit Choudhary ◽  
Rakesh Kumar Singh
Keyword(s):  
Prediction Model ◽  
Open Source ◽  
Open Source Software ◽  
Prediction Models ◽  
New Technology ◽  
Modern Technology ◽  
Time Frequency ◽  
Rigorous Testing ◽  
Technology Changes ◽  
Sensitivity Specificity

In today's world, the heart of modern technology is software. In order to compete with pace of new technology, changes in software are inevitable. This article aims at the association between changes and object-oriented metrics using different versions of open source software. Change prediction models can detect the probability of change in a class earlier in the software life cycle which would result in better effort allocation, more rigorous testing and easier maintenance of any software. Earlier, researchers have used various techniques such as statistical methods for the prediction of change-prone classes. In this article, some new metrics such as execution time, frequency, run time information, popularity and class dependency are proposed which can help in prediction of change prone classes. For evaluating the performance of the prediction model, the authors used Sensitivity, Specificity, and ROC Curve. Higher values of AUC indicate the prediction model gives significant accurate results. The proposed metrics contribute to the accurate prediction of change-prone classes.

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