Climate change is increasingly affecting the condition and lifespan of roads across Canada through rising temperatures, heavier rainfall, and more frequent extreme weather events. These factors, combined with heavier traffic and overloaded trucks, accelerate road deterioration, leading to more frequent maintenance and rehabilitation needs and significantly higher costs. In the Niagara Region of Ontario, the number of days exceeding 30°C is projected to rise from 7 days historically to 31 days by the 2050s and 45 days by the 2080s. This warming trend directly threatens pavement integrity and service life.

In the preliminary phase of a project in Niagara Region, the impact of future climate conditions on road network performance was examined. Local traffic and pavement condition data were collected from 97 road sections between 2016 and 2023, and a Recursive Ridge Regression model was developed to forecast pavement roughness progression under two climate scenarios, SSP2-4.5 (medium emissions) and SSP5-8.5 (high emissions). Maximum pavement temperature was identified as the dominant driver of long-term deterioration, primarily through asphalt rutting. By 2050, road conditions under the high-emission scenario are projected to be approximately 2.44% worse on average than under the medium-emission scenario, with the gap widening in later decades. As a result, current maintenance schedules and budgets may no longer be sufficient to keep roads in good condition under higher emission climate scenarios.

Therefore, incorporating climatic projections into pavement management systems and adjusting maintenance strategies accordingly is essential. This approach can help municipalities like Niagara Region make smarter, longer-term investments in road infrastructure, with future work expanding the methodology to include additional climate stressors, surface distress types, and treatment scenarios across the broader road network.

Contributors: Barzegaran J, Dezfoulian R S, Hossain K.

This study investigates the sensitivity of flexible pavement performance to both
historical and projected climate conditions across five Canadian cities over four 25-
year design cycles (2000–2099). Hourly climate inputs from MERRA2 and dynamically
downscaled CanRCM4 outputs under RCP 2.6, 4.5, and 8.5 were processed using the
Pavement Mechanistic-Empirical Design (PMED v3.21) tool. Climate projections were
stratified as maximum, median, and minimum ensemble members for each RCP to
account for the uncertainty. Five primary distress responses (total and asphalt
permanent deformation, bottom-up fatigue cracking, thermal cracking, and international roughness index) were simulated under each scenario and compared against historical baseline. Results reveal a significant increase in mean annual air temperature and precipitation, with pronounced reductions in freezing indices and freeze-thaw cycles, especially under high-emission RCP 8.5 scenario with maximum ensemble projection. Climate-driven increases in temperature substantially accelerate the rates of rutting and fatigue cracking, leading to premature loss of service life up to 66% in asphalt permanent deformation for the worst-case scenario (RCP 8.5 and 2075-2099 design period), while simultaneously reducing thermal cracking risks. The findings demonstrate marked regional variability, with the Prairie provinces being most
vulnerable due to climate change. The study underscores the inadequacy of relying on
historical climate data such as MERRA2 and emphasizes incorporating high-resolution region-specific climate projections with conservative RCP scenarios into pavement design protocols to ensure long-term infrastructure resilience in the era of climate change.

Contributors: Khan S, Hossain K, Maadani O, Gaur A.

Routine pavement maintenance and rehabilitation are the most crucial parts of the pavement management system (PMS) to keep the roads in serviceable condition. Depending on the severity of the surface deterioration, rehabilitation is prioritized over maintenance to increase the pavement’s lifespan. The International Roughness Index (IRI) serves as a key functional performance indicator for assessing overall surface roughness and guiding the selection of appropriate treatment types. Therefore, an efficient IRI model capturing the influence of climate and treatment types is the prerequisite to making any fruitful decision on proper treatment types. Utilizing the United States flexible pavement sections data from the Long-Term Pavement Performance (LTPP) program, the research examined the effectiveness of different rehabilitation treatments across various climate conditions through the IRI prediction models. The fuzzy c-means clustering technique was used to categorize the climate conditions into three clusters. Moreover, feature importance analysis was performed to evaluate the influential variables in IRI prediction model. This study employed multiple linear regression (MLR) and artificial neural networks (ANNs) to predict IRI, leveraging machine learning techniques to capture both linear and non-linear relationships among variables. The results revealed that both models performed well under different climatic conditions with reasonable accuracy (R2 > 80% and MSE < 0.2). The results showed that milling the existing pavement and overlay with hot mix recycled asphalt concrete (MILL_REC_AC_OL) consistently outperformed the control asphalt concrete overlay (AC_OL) in all climatic conditions, while recycled asphalt overlay (REC_AC_OL) was more effective than AC_OL in moderate and hot climate. Warm mix asphalt overlay (WMA_OL) seemed to perform better than AC_OL in cold and moderate climate conditions but less effective in hot regions.

Contributors: ĚýKhan S, Barzegaran J, Swarna S,ĚýHossain K.

Contributors: Khan S,ĚýMaadani O, Hossain K.

Contributors: ĚýKhan S, Barzegaran J, Hossain K, Swarna S.

Contributors: ZawadĚý F S, Hossain K,ĚýBarzegaran J.

Contributors: Chowdhury S A, Hossain K,ĚýBarzegaran J.

Authors: Rana M, Swarna S,ĚýHossain K, Hakim M A, Mujib H I.

In an asphalt structure, mastic is the primary element that deforms. Many studies have been conducted to develop a rheological parameter that can assess the deformation and creep characteristics of asphalt mastic with varying proportions of fillers. However, only a few studies show the influence of Hydrated Lime (HL), Limestone (LS), and their combined effect (HL + LS) on the creep recovery performance of asphalt mastic with the combination of modifiers and anti-stripping agents. This paper employs the multiple stress creep recovery (MSCR) test as per AASHTO T 350 to understand the creep recovery properties of asphalt mastic. This study modified a neat PG 58-28 binder with Styrene–Butadiene–Styrene (SBS) or Gilsonite and Zycotherm as a liquid anti-stripping agent. Different filler-binder (F/B) ratios of HL, LS, and different proportions of HL + LS combined with SBS and Gilsonite modified binder containing Zycotherm were utilized to fabricate the asphalt mastic. Then, the Rolling Thin-Film Oven (RTFO) protocol was applied to simulate asphalt production time aging. The performance of these mastics was compared using non-recoverable creep compliance, stress sensitivity analysis, and MSCR percent recovery analysis. AASHTO M 332 specifications have been used to classify all the mastics based on the Jnr value at 3.2ĚýkPa and stress sensitivity. In addition, polymer modification curves specified by the asphalt institute (AI) were employed to interpret the test results. According to the analysis of experimental data, the combined effect of 10% HL and 70% LS modified with SBS was observed to be predominant and satisfy all the creep recovery performance requirements.

Authors: Feroz S I, Alfalah A, MitraĚýD, Hossain K, Lawlor M, .

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This chapter presents a comprehensive field investigation into the ice melting capacities of road salt under extreme cold weather conditions in Ottawa, Canada. The study focuses on the effectiveness of sodium chloride, a commonly used deicing agent, and its performance under varying environmental factors such as air temperature, pavement temperature, and snow conditions. The research involved conducting multiple experiments during the winter season of 2022, applying different rates of road salt to a parking lot and observing its effectiveness in melting ice and snow. The results indicate that higher application rates of road salt generally led to better performance, but lower rates were often ineffective, especially in colder temperatures. The study also highlights the importance of environmental factors like sunlight exposure on the effectiveness of deicing agents. This research is part of a multi-year project aimed at evaluating environmentally friendly deicing and anti-icing chemicals, with a particular focus on understanding the limitations and potential of sodium chloride in harsh winter conditions.

Authors: Lawlor M,ĚýHossain K, Rana M.

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