Artificial neural network model for forecasting energy consumption in hot mix asphalt (HMA) production

Androjić, Ivica; Dolaček-Alduk, Zlata

doi:10.1016/j.conbuildmat.2018.03.086

Cited by 22 publications

(6 citation statements)

References 8 publications

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“…Androjić and Dolaček-Alduk [79] examined the influence of different types of asphalt mixtures, humidity content, hourly capacity, and production temperature to anticipate the natural gas consumption in the process of producing HMA using MLNPN. Meanwhile, aggregate temperature showed to be a vital factor affecting the consumption of energy in HMA generation.…”

Section: Flexible Pavement Constructionmentioning

confidence: 99%

Artificial Neural Networks for Flexible Pavement

et al. 2023

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Transportation agencies are primarily responsible for building new roads and maintaining current roads. The main focuses of these agencies are to prioritize maintenance and make significant rehabilitation decisions to handle serious problems facing road authorities. Considerable efforts and an abundance of studies have been performed to determine the nature, mechanisms, test methods, and measurement of pavements for preservation and improvements of roadways. The presented study reports a state-of-the-art review on recent advances in the application of artificial intelligence in various steps of flexible pavement, including pavement construction, performance, cost, and maintenance. Herein, the challenges of gathering large amounts of data, parameter optimization, portability, and low-cost data annotating are discussed. According to the findings, it is suggested that greater attention should be paid to integrating multidisciplinary roadway engineering techniques to address existing challenges and opportunities in the future.

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Section: Flexible Pavement Constructionmentioning

confidence: 99%

Artificial Neural Networks for Flexible Pavement

et al. 2023

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“…The authors trained three different ANNs to analyze datasets. Androjić and Dolaček-Alduk [26] provided an ANN model with the objective of predicting the consumption of natural gas during asphalt production. Tosun et al [27] used linear regression and artificial neural network modeling to predict engine performance.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Dynamic Stiffness of Resilient Materials using Artificial Neural Network (ANN) Technique

2019

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High-rise residential buildings are constructed in countries with high population density in response to the need to utilize small development areas. As many high-rise buildings are being constructed, issues of floor impact sound tend to occur in buildings. In general, resilient materials are implemented between the slab and the finishing mortar to control the floor impact sound. Various mechanical properties of resilient materials can affect the floor impact sound. To investigate the impact sound reduction capacity, various experimental tests were conducted. The test results show that the floor impact sound reduction capacity has a close relationship with the dynamic stiffness of resilient materials. A total of six different kinds of resilient materials were loaded under four loading conditions. The test results show that loading time, loading, and material properties influence the change in dynamic stiffness. Artificial neural network (ANN) technique was implemented to obtain the responses between the deflection and dynamic stiffness. Three different algorithms were considered in the ANN models and the trained results were analyzed based on the root mean square error. The feasibility of using the ANN technique was verified with a high and consistent level of accuracy.

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“…Data mining is generally used to obtain patterns or models from databases applying specific algorithms to retrieve useful knowledge from data, in this case, collected during the tests with the prototype. There are many regression methods that can be employed in data mining, among which artificial neural networks (ANN) (Androjić and Dolaček-Alduk, 2018;Basheer and Hajmeer, 2000), support vector machines (SVM) (Burges, 1998;Naseri et al, 2017), k-nearest neighbors (Aksoy et al, 2012;Seidl and Kriegel, 1998) and regression trees (Chou et al, 2014;Loh, 2011) can be mentioned.…”

Section: Introductionmentioning

confidence: 99%

“…The use of data mining techniques in the field of road pavements is not original, but it has not yet been applied to predict the energy consumption of motor vehicles due to the roadpavement interaction. Nevertheless, data mining was already used to predict the rolling resistance of an agricultural tractor tire moving over a clay loam soil (Taghavifar et al, 2013), and to forecast energy consumption in asphalt plants during hot mix asphalt production (Androjić and Dolaček-Alduk, 2018). Furthermore, this work has also based its development on examples of other data mining applications in road pavements, such as those presented in the following paragraphs.…”

Section: Introductionmentioning

confidence: 99%

Estimation of energy consumption on the tire-pavement interaction for asphalt mixtures with different surface properties using data mining techniques

Araujo

Palha

Martins

et al. 2019

Transportation Research Part D: Transport and Environment

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The energy or fuel consumption of the millions of vehicles that daily operate in road pavements has a significant economic and environmental impact on the use phase of road infrastructures regarding their life cycle analysis. Therefore, new solutions should be studied to reduce the vehicles energy consumption, namely due to the tire-pavement interaction, and contribute towards the sustainable development. This study aims at estimating the energy consumption due to the rolling resistance of tires moving over pavements with distinct surface characteristics. Thus, different types of asphalt mixtures were used in the surface course to determine the main parameters influencing the energy consumption. A laboratory scale prototype was developed explicitly for this evaluation. Data mining techniques were used to analyze the experimental results due to the complex correlation between the data collected during the tests, providing meaningful results. In particular, the artificial neural network allowed to obtain models with excellent capacity to estimate energy consumption. A sensitive analysis was carried out with a five input parameter model, which showed that the main parameters controlling the energy consumption are the vehicle speed and the mean texture depth.

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Artificial neural network model for forecasting energy consumption in hot mix asphalt (HMA) production

Cited by 22 publications

References 8 publications

Artificial Neural Networks for Flexible Pavement

Artificial Neural Networks for Flexible Pavement

Prediction of the Dynamic Stiffness of Resilient Materials using Artificial Neural Network (ANN) Technique

Estimation of energy consumption on the tire-pavement interaction for asphalt mixtures with different surface properties using data mining techniques

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