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.

CALIBRATION OF RUTTING AND ROUGHNESS DISTRESS MODELS OF HDM-4 FOR DEVELOPING PAVEMENT MAINTENANCE MANAGEMENT

2018 ◽  
Vol 5 (2) ◽  
Author(s):  
Aditya Singh ◽  
Tanuj Chopra
Keyword(s):  
Road Network ◽  
Local Conditions ◽  
Pavement Condition ◽  
Pavement Maintenance ◽  
The Road ◽  
The World Bank ◽  
Pavement Deterioration ◽  
Network Maintenance ◽  
Investment Programme ◽  
Development And Management

The Highway Development and Management model (HDM-4) is a tool developed by the World Bank to aid highway administrators and engineers in the process of decision making for preparing of road investment programme and determining the road network maintenance strategies. HDM-4 essentially models the interaction between the traffic volume, environment and pavement composition to predict the different kinds of distress that develop in pavements over time. Since distress is caused due to different conditions and progresses at different rates, therefore it is necessary to calibrate the HDM-4 model as per the local conditions. The aim of the study is to calibrate the HDM-4 pavement deterioration model in terms of rutting and roughness for the urban road network of Patiala (Punjab, India). In our study, we select 15 road sections and group them based on varying traffic and pavement age. The pavement condition data, which was measured starting from 2012 to the end of 2014, is fed as the input to the HDM-4 distress models. The calibration process is performed using statistical analysis between the observed and predicted value of the distress by keeping minimum Root Mean Square Error (RMSE) and maximum R-square (R2). The determined calibration factors are validated and further used for developing pavement deterioration models which prove to be helpful in building a Pavement Maintenance and Management system for Patiala.

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.

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 The Accuracy of the Altman, Ohlson, Springate and Zmejewski Models in Bankruptcy Predicting Trade Sector Companies in Indonesia

2020 ◽  
Vol 3 (1) ◽  
pp. 334-347 ◽  
Author(s):  
Sri Elviani ◽  
Ramadona Simbolon ◽  
Zenni Riana ◽  
Farida Khairani ◽  
Sri Puspa Dewi ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Prediction Model ◽  
Prediction Models ◽  
Binary Logistic Regression ◽  
Bankruptcy Prediction ◽  
Accurate Model ◽  
Analysis Technique ◽  
Trade Sector ◽  
Altman Model

Bankruptcy prediction models continue to develop both in terms of forms, models, formulas, and analysis systems. Various bankruptcy prediction studies currently conducted aim to find the most appropriate and accurate bankruptcy prediction model to be used in predicting bankruptcy. This study aims to determine the most appropriate and accurate model in predicting the bankruptcy of 53 trade sector companies in Indonesia. The analysis technique used in this study is binary logistic regression. The results of this study prove that the most appropriate and accurate model in predicting bankruptcy of trade sector companies in Indonesia is the Springate model and the Altman model

The Highway Development and Management System in Washington State

2005 ◽  
Vol 1933 (1) ◽  
pp. 52-61 ◽  
Author(s):  
Jianhua Li ◽  
Stephen T. Muench ◽  
Joe P. Mahoney ◽  
Nadarajah Sivaneswaran ◽  
Linda M. Pierce ◽  
...  
Keyword(s):  
Management System ◽  
Road Network ◽  
The United States ◽  
Washington State ◽  
Pavement Management ◽  
Portland Cement Concrete ◽  
Long Term Effects ◽  
Local Conditions ◽  
Development And Management

The Highway Development and Management System (HDM-4) developed by the World Bank is a powerful pavement management software tool capable of performing technical and economic appraisals of road projects, investigating road investment programs, and analyzing road network preservation strategies. Its effectiveness is dependent on the proper calibration of its predictive models to local conditions. Although significant work has been done in calibrating and applying HDM-4 worldwide (especially in developing nations), no substantial effort has been made within the United States. This paper describes the calibration and application of HDM-4 (Version 1.3) to the Washington State Department of Transportation's (WSDOT) road network. WSDOT hopes to use HDM-4 to supplement its existing Washington State Pavement Management System (WSPMS) in long-term pavement performance and financial needs. Significant findings are that ( a) HDM-4 can be used to analyze the WSDOT road network, ( b) HDM-4 was successfully calibrated for the network, ( c) the network requires calibration factors significantly different than HDM-4 default values, ( d) software issues seem to prevent use of HDM-4 portland cement concrete pavement analysis, and ( e) WSDOT can use HDM-4 to predict pavement preservation budgets quickly, select optimal preservation strategies under varying budget levels, and assist in determining the long-term effects of different funding scenarios on the road network.

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.

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 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.

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