A Pavement Condition-Rating Model Using Backpropagation Neural Networks

1995 ◽  
Vol 10 (6) ◽  
pp. 433-441 ◽  
Author(s):  
Neil N. Eldin ◽  
Ahmed B. Senouci
Keyword(s):  
Neural Networks ◽  
Pavement Condition ◽  
Backpropagation Neural Networks ◽  
Condition Rating ◽  
Rating Model

Development of Pavement Condition Rating Model and Pavement Roughness Model for Saudi Highways

2013 ◽  
Vol 723 ◽  
pp. 820-828 ◽  
Author(s):  
Muhammad Mubaraki
Keyword(s):  
Iri Model ◽  
International Roughness Index ◽  
Pavement Condition ◽  
Safety Issues ◽  
Condition Rating ◽  
Pavement Roughness ◽  
Roughness Model ◽  
Pavement Deterioration ◽  
Total Observation ◽  
Rating Model

The Pavement Condition Rating (PCR) has been used by the Ministry of Transport (MOT) in Saudi Arabia to report pavement condition. The World Bank developed the PCR in 1986. PCR is based on International Roughness Index (IRI), Rutting (RUT), Cracking (CRA), and Raveling (RAV). The MOT collects pavement condition data using a digital inspection vehicle called Road Surface Tester (RST) vehicle. On some expressways, the MOT measures the Skid Number (SN) using a Skid Test Unit as complimentary measurement for safety issues. The objective of this paper is to develop PCR model and pavement roughness model using survey data for overlaid sections on some expressways in the network with total observation number is 3469. The PCR model is a function of pavement age (T), Traffic Volume (TV), and IRI. The IRI model is a function of RUT, RAV, and CRA. Overlaid sections across the entire network have been selected to study the mechanisms of pavement deterioration, to develop the model and to draw conclusions.

Forecasting Overall Pavement Condition with Neural Networks: Application on Florida Highway Network

2003 ◽  
Vol 1853 (1) ◽  
pp. 3-12 ◽  
Author(s):  
Jidong Yang ◽  
Jian John Lu ◽  
Manjriker Gunaratne ◽  
Qiaojun Xiang
Keyword(s):  
Neural Networks ◽  
Surface Condition ◽  
Composite Index ◽  
Critical Issue ◽  
Pavement Management ◽  
Pavement Condition ◽  
Pavement Surface ◽  
Condition Rating ◽  
Highway Network ◽  
Ann Models

Timely identification of undesirable crack, ride, and rut conditions is a critical issue in pavement management systems at the network level. The overall pavement surface condition is determined by these individual pavement surface conditions. A research project was carried out to implement an overall methodology for pavement condition prediction that uses artificial neural networks (ANNs). In the research, three ANN models were developed to predict the three key indices—crack rating, ride rating, and rut rating—used by the Florida Department of Transportation (FDOT) for pavement evaluation. The ANN models for each index were trained and tested by using the FDOT pavement condition database. In addition to the three key indices, FDOT uses a composite index called pavement condition rating (PCR), which is the minimum of the three key indices, to summarize overall pavement surface condition for pavement management. PCR is forecast with a combination of the three ANN models. Results of the research suggest that the ANN models are more accurate than the traditional regression models. These ANN models can be expected to have a significant effect on FDOT's pavement management system.

scholarly journals Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-Time-Intervals Using Particle Swarm Optimization Algorithm

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1213
Author(s):  
Ahmed Aljanad ◽  
Nadia M. L. Tan ◽  
Vassilios G. Agelidis ◽  
Hussain Shareef
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Solar Irradiance ◽  
Particle Swarm ◽  
Time Interval ◽  
Time Intervals ◽  
Swarm Optimization ◽  
Backpropagation Neural Networks ◽  
Proposed Model ◽  
Short Time

Hourly global solar irradiance (GSR) data are required for sizing, planning, and modeling of solar photovoltaic farms. However, operating and controlling such farms exposed to varying environmental conditions, such as fast passing clouds, necessitates GSR data to be available for very short time intervals. Classical backpropagation neural networks do not perform satisfactorily when predicting parameters within short intervals. This paper proposes a hybrid backpropagation neural networks based on particle swarm optimization. The particle swarm algorithm is used as an optimization algorithm within the backpropagation neural networks to optimize the number of hidden layers and neurons used and its learning rate. The proposed model can be used as a reliable model in predicting changes in the solar irradiance during short time interval in tropical regions such as Malaysia and other regions. Actual global solar irradiance data of 5-s and 1-min intervals, recorded by weather stations, are applied to train and test the proposed algorithm. Moreover, to ensure the adaptability and robustness of the proposed technique, two different cases are evaluated using 1-day and 3-days profiles, for two different time intervals of 1-min and 5-s each. A set of statistical error indices have been introduced to evaluate the performance of the proposed algorithm. From the results obtained, the 3-days profile’s performance evaluation of the BPNN-PSO are 1.7078 of RMSE, 0.7537 of MAE, 0.0292 of MSE, and 31.4348 of MAPE (%), at 5-s time interval, where the obtained results of 1-min interval are 0.6566 of RMSE, 0.2754 of MAE, 0.0043 of MSE, and 1.4732 of MAPE (%). The results revealed that proposed model outperformed the standalone backpropagation neural networks method in predicting global solar irradiance values for extremely short-time intervals. In addition to that, the proposed model exhibited high level of predictability compared to other existing models.

PREDIKSI PERTUMBUHAN PENDUDUK PADA PROVINSI SUMATERA BARAT DENGAN MENGGUNAKAN METODE BACKPROPAGATION

2020 ◽  
Vol 7 (4) ◽  
pp. 216
Author(s):  
Hijrah Yanti Sitanggang ◽  
Vera Irma Delianti
Keyword(s):  
Neural Networks ◽  
Growth Rate ◽  
Artificial Neural Networks ◽  
Population Growth ◽  
Population Growth Rate ◽  
Backpropagation Neural Networks ◽  
West Sumatra ◽  
Artificial Neural ◽  
The City

The problem of population is one of the problems in the Province of West Sumatra, especially in the City of Padang, Kota Bukitinggi, and the City of Payakumbuh which has a very fast population growth rate, this occurs due to several factors such as births, deaths, residents who come, and residents who leave. The highest population growth occurred in Padang City in 2018 amounting to 939,112 residents and the smallest population growth occurred in the City of Bukitinggi in 2014 amounting to 120,491 residents. The purpose of this study is to predict population growth that will occur in 2019 in the cities of Padang, Bukittinggi and Payakumbuh. The method used in this research is descriptive correlational by applying backpropagation neural networks. The application used is Matlab. Based on the problems and methods obtained, the predicted results in 2019 in Padang City amounted to 124,7150, Bukittinggi numbered 126,8040 and Payakumbuh totaled 128.7830.  Keywords: Artificial Neural Networks, Backpropagation, Matlab.

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