Computerized decision tree for anti-icing/pretreatment applications as a result of laboratory and field testings

Ikiz, Nida Noorani; Galip, E.

doi:10.1016/j.coldregions.2016.03.004

Cited by 11 publications

(6 citation statements)

References 2 publications

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“…Similarly, İkiz and Galip () estimated the decrease in the salt concentration over time. They also used a mathematical function based on traffic and time.…”

Section: Literature Reviewmentioning

confidence: 99%

Street‐segment‐based salt and abrasive prediction for winter maintenance using machine learning and GIS

Ahabchane

Trépanier

2018

Transactions in GIS

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Salting operations are essential but expensive in northern winters. We aim to predict the quantity of salt and abrasive needed for a specific road segment for each hour. An estimate of the quantity allows managers to better manage the loads in the trucks and to propose optimal vehicle routes based on the forecast, which will allow them to optimize costs. This article uses machine‐learning techniques based on truck telemetry data, weather conditions, and segment attributes. Geographic information systems (GIS) allow us to exploit the street‐network characteristics, which were ignored by previous prediction models. The results show that the XGBoost method performs better than other techniques (R2 = 0.83).

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“…Similarly, İkiz and Galip () estimated the decrease in the salt concentration over time. They also used a mathematical function based on traffic and time.…”

Section: Literature Reviewmentioning

confidence: 99%

Street‐segment‐based salt and abrasive prediction for winter maintenance using machine learning and GIS

Ahabchane

Trépanier

2018

Transactions in GIS

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“…The long-term anti-condensation ice model of pavements was established by indoor salt precipitation tests. In addition to the anti-condensation ice model, İkiz and Galip [ 27 ] studied the decay law of salt in porous pavements and cement concrete pavements with time and traffic volume, establishing a residual salt prediction model.…”

Section: Introductionmentioning

confidence: 99%

Laboratory Study and Field Validation of the Performance of Salt-Storage Asphalt Mixtures

Cao

Liu

et al. 2022

Materials

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The traditional method of removing ice and snow on roads carries the risk of damaging roads and the environment. In this circumstance, the technology of salt-storage asphalt pavement has gradually attracted attention. However, snow-melting salts may also have an impact on asphalt mixture performance. To explore the effect of snow-melting salts on the mechanical and surface properties of salt-storage asphalt mixtures (SSAM), SSAMs were prepared with styrene–butadiene–styrene (SBS)-modified asphalt and high-elastic asphalt (HEA) as binders and snow-melting salts as fillers. The influence of the type of asphalt binder and the content of snow-melting salt on the performance of the SSAM was preliminarily investigated through laboratory tests. The results show that the high-temperature, low-temperature, and moisture resistance performance of the SBS group SSAM decreased by 9.8–15.1%, 1.6–12.3%, and 6.3–19.4%, respectively, compared with SBS00. The higher the amount of snow-melting salt, the greater the performance drop. The three mechanical properties of the HEA group containing high-elastic agent TPS are 11.3–19.7%, 4.2–12.3%, and 4.8–13.3% higher than that of the SBS group. Even when the content of snow-melting salt is 50% or 75%, the mechanical properties of the HEA group are better than that of SBS00 without snow-melting salt. Snow-melting salt has clear advantages in improving the anti-skid performance but decreases the anti-spalling performance. The surface properties of the HEA group were also better than that of the SBS group. Considering the mechanical properties and surface properties, the comprehensive performance of the HEA group is better than that of the SBS group, and HEA50 has the best comprehensive performance. In addition, the construction performance of the SSAM has also been verified, and the production of SSAM according to the hot mix asphalt can meet the specification requirements.

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“…Whereas in the past the production of de-icing agents was only possible through the importation of chloride compounds, in the last 25 years, there have been several possibilities for the production of chloride de-icing agents even at home. Very interesting for the decision-making process can be the computer decision tree for the application of de-icing agents [25], which could increase the efficiency of winter maintenance processes in our conditions.…”

Section: Introductionmentioning

confidence: 99%

Effect of Winter Road Maintenance on the Asphalt Road Surface—Experience in Slovakia and the Czech Republic

2022

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The properties of asphalt mixtures and the quality of their execution are key factors for their service life and durability in the pavement structure. This research aims to study the effect of deicing chemicals (sodium chloride, NaCl) on asphalt mixtures in laboratory conditions to know the changes in properties when the samples were loaded with a different number of freeze–thaw cycles. The behavior of the standardly used asphalt mixtures and bitumen binders was verified by the exposure to sodium chloride solution (20 g/L NaCl). In the first part of the experiment, penetration, elastic recovery, and softening point tests were performed for paving grade bitumen 50/70 and 70/100, and polymer-modified bitumen PMB 25/55-60. Furthermore, asphalt concrete (AC 11) mixtures with different air voids were subjected to 25, 50, and 75 freeze–thaw cycles to determine the effect on the service life of the pavement structure. Following the Czech standard, CSN 73 6161, and the TP 170 regulation for the design of asphalt pavements, the Marshall test and the stiffness modulus were determined for all asphalt samples. The obtained results show a negative effect of freeze–thaw cycles on the properties of asphalt concrete surface course mixtures and bitumen.

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Computerized decision tree for anti-icing/pretreatment applications as a result of laboratory and field testings

Cited by 11 publications

References 2 publications

Street‐segment‐based salt and abrasive prediction for winter maintenance using machine learning and GIS

Street‐segment‐based salt and abrasive prediction for winter maintenance using machine learning and GIS

Laboratory Study and Field Validation of the Performance of Salt-Storage Asphalt Mixtures

Effect of Winter Road Maintenance on the Asphalt Road Surface—Experience in Slovakia and the Czech Republic

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