Winter Operations Research Articles

Evaluating Mobile LiDAR Intensity Data for Inventorying Durable Tape Pavement Markings.

Good visibility of lane markings is important for all road users, particularly autonomous vehicles. In general, nighttime retroreflectivity is one of the most challenging marking visibility characteristics for agencies to monitor and maintain, particularly in cold weather climates where agency snowplows remove retroreflective material during winter operations. Traditional surface-applied paint and glass beads typically only last one season in cold weather climates with routine snowplow activity. Recently, transportation agencies in cold weather climates have begun deploying improved recessed, durable pavement markings that can last several years and have very high retroreflective properties. Several dozen installations may occur in a state in any calendar year, presenting a challenge for states that need to program annual repainting of traditional waterborne paint lines, but not paint over the much more costly durable markings. This study reports on the utilization of mobile mapping LiDAR systems to classify and evaluate pavement markings along a 73-mile section of westbound I-74 in Indiana. LiDAR intensity data can be used to classify pavement markings as either tape or non-tape and then identify areas of tape markings that need maintenance. RGB images collected during LiDAR intensity data collection were used to validate the LiDAR classification. These techniques can be used by agencies to develop accurate pavement marking inventories to ensure that only painted lines (or segments with missing tape) are repainted during annual maintenance. Repeated tests can also track the marking intensity over time, allowing agencies to better understand material lifecycles.

4E Analysis of a new cogeneration system coupling heat pumps and PEMFC in summer and winter modes

In response to the ongoing optimization of energy systems and the promotion of clean energy, this paper introduces a new high-efficiency cogeneration system based on proton exchange membrane fuel cells (PEMFC). Named the Transcritical Combined Cooling Heating and Power with Subcooling Coupled ORC (CPTSO) system, it undergoes energy, exergy, economic, and environmental (4E) assessments to evaluate its performance in both summer and winter modes. This paper also analyzes the effects of key factors on the new system's performance. The results indicate that the new system performs more effectively overall and achieves faster cost recovery during winter operations. With varying degrees of subcooling (ΔTsubcooling), the system's average energy efficiency, fuel energy saving ratio (FESR), exergy efficiency, and pollutant reduction in winter are 60.97 %, 4.51 %, 17.16 %, and 33.35 % higher, respectively, than in summer. Changes in the main evaporation temperature (Te) result in winter improvements of 74.37 % in energy efficiency, 16.86 % in FESR, 17.6 % in exergy efficiency, and 45.5 % in pollutant reduction compared to summer. Similarly, adjustments to the outlet temperature of the gas cooler (Tg) lead to winter increases of 56.75 % in energy efficiency, 0.49 % in FESR, 17.1 % in exergy efficiency, and 29.3 % in pollutant reduction over summer values.

Multiscale evaluation of asphalt binder-aggregate interface exposed to sodium chloride deicer

ABSTRACT Sodium chloride (NaCl) is widely used in roadways’ winter maintenance operations. Despite its effectiveness, NaCl solution is known to accelerate the moisture-induced damage process in asphalt mixes. This study aimed to identify the mechanisms involved in the deterioration of adhesive and cohesive bonds in asphalt and aggregate interfaces due to the NaCl deicer and freeze–thaw (F-T) action. A multiscale material characterisation approach and evaluation of fundamental intrinsic material properties were applied to achieve this goal. Adhesive properties of aggregate-binder systems subjected to aqueous NaCl concentrations and F-T cycles were characterised by conducting binder bond strength tests. The surface free energy technique was also employed to evaluate the impact of salt concentration and moisture on the different energy parameters and moisture-induced damage potential. Furthermore, the influence of salt concentration and F-T cycles on the surface nanostructure and adhesion of the asphalt binder was evaluated using atomic force microscopy. The multiscale characterisation approach presented in this study provided significant insights into the physio-chemical mechanisms involved in the deterioration of the bonds in aggregate-binder systems exposed to different aqueous NaCl concentrations. A fundamental understanding of the damage mechanisms is necessary to develop counteracting measures that extend the life of asphalt pavements in cold climates.

Perceptions of and adaptation to changing winter weather by logging business owners in northern Minnesota, USA

ABSTRACT Focus groups with northern Minnesota logging business owners were held in spring 2023 to understand perceived and observed winter weather changes, challenges they may be facing due to any winter weather changes, and strategies they are taking to adapt to changing conditions. These discussions focused on logger perceptions of weather-related changes during the winter logging season over the course of their career, impacts of weather-related changes on harvest productivity, changes in business practices due to weather-related changes, and positive impacts due to winter weather changes. Most focus group participants are perceiving changing winter weather conditions and experiencing additional challenges to their winter logging operations due to these changes. They described how winter weather changes are impacting harvesting production and profitability, as well as how they have adapted their logging practices in response to these changes. The evolution of logging equipment and the characteristics of timber tracts available to harvest in winter, as well as greater sensitivity to soil rutting and compaction, make it difficult to discern the extent to which their observed changes in winter logging operations can be attributed solely to weather-related changes.

Weather event characterization: a catalyst for improved spatial mapping and benefit quantification in winter road maintenance

Monitoring winter road surface conditions (RSC) is essential for optimizing winter road maintenance operations and ensuring public traffic safety and mobility. However, many existing RSC estimation methods focus primarily on local or site-specific conditions, thus lacking effectiveness in comprehensive spatial mapping under varying winter weather events. Additionally, the potential financial benefits these methods could provide to maintenance authorities remain largely unexplored. This study addresses these gaps by introducing a refined methodology that harnesses the K-Means algorithm to characterize various weather events – a significant stride towards improving the generalizability and accuracy of road surface temperature (RST) estimations. The study also incorporates regression kriging (RK), a renowned geostatistical technique, as an integral component of a system for constructing an extensive spatial map of RSC across large highway networks. This strategy maximizes the use of data from existing road weather information systems (RWIS) to bridge their large spatial gaps. This study further quantifies the potential savings derived from optimally placing RWIS stations and reducing traffic collisions. The efficacy of these methods is validated through a real-world case study on two major interstate highways in Iowa, US, with RST estimation discrepancies as low as 0.619 °C. The findings also indicate potential cost savings by conserving up to 10 RWIS stations, which can be further translated into monetary savings of RWIS capital costs, traffic collisions prevention, enhanced traffic mobility and savings in maintenance material usage.

Investigation of winter weather crash injury severity using winter storm classification techniques

This study employs a unique methodology for analyzing seasonal traffic crashes by first identifying adverse winter-weather events using traditional traffic-crash data (from Nebraska) and correlating them to detailed meteorological data for subsequent analyses. Specifically, the novel component of this analysis utilized a state-specific winter storm and seasonal severity index metric in conjunction with classifications of storm type. Both crash and meteorological data were combined to determine if the amount of snowfall and/or storm intensity/severity could be a predictor of the number and severity of crashes. Analysis was performed to identify weather conditions causing highway safety concerns. The important takeaway of the study was that most winter weather-related vehicular crashes in Nebraska were associated with relatively minor winter weather conditions. The reported crashes typically occurred with relatively low snowfall amounts due to residual snowfall on the ground, emphasizing the importance of continuing winter maintenance operations activities and public service announcements long after a storm has passed through the region. Modeling crash injury severity revealed that icy pavements, higher duration of snowfall, work zones, and higher number of trucks and buses involved in a crash were linked to higher injury severity; higher visibility was associated with a greater chance of crashes involving visible injuries but a lower likelihood of crashes involving disabling injury/fatality. Snowfall was associated with more visible injury crashes, whereas snow-depth was linked to fewer visible injury and disabling injury/fatality crashes. The study's limitations include the spatial and temporal aggregation of weather data and a lack of detailed winter roadway maintenance/clearance activity data.

Statewide Implementation of Salt Stockpile Inventory Using LiDAR Measurements: Case Study

The state of Indiana maintains approximately 120 salt storage facilities strategically distributed across the state for winter operations. In April 2023, those facilities contained approximately 217,000 tons of salt with an estimated value of USD 21 million. Accurate inventories at each facility during the winter season are important for scheduling re-supply so the facilities do not run out of salt. Inventories are also important at the end of the season for restocking to provide balanced inventories. This paper describes the implementation of a portable pole-mounted LiDAR system to measure salt stockpile inventory at 120 salt storage facilities in Indiana. Using two INDOT staff members, the end-of-season inventory took 9 working days, with volumetric inventories provided within 24 h of data collection. To provide an independent evaluation of the methodologies, the Hovermap ST backpack was used at selected facilities to provide control volumes. This system has a range of 100 m and an accuracy of ±3 cm, which reduces the occlusion to less than 8%. The pre-season facility capacity ranged from 0% to 100%, with an average of 66% full across all facilities. The post-season facility percentage ranged from 3% to 100%, with an average of 70% full. In addition, permanent roof-mounted LiDAR systems were deployed at two facilities to evaluate the effectiveness of monitoring salt stockpile inventories during winter operation activities. Plans are now underway to install fixed LiDAR systems at 15 additional facilities for the 2023–2024 winter season.

Development of a surrogate model of a trans-critical CO2 heat pump for use in operations optimization using an artificial neural network

Conventional physics-based models can demand substantial computational resources when employed for operational optimization. To allow faster system simulations that can be employed for operational optimization, a surrogate model of the CO2 heat pump has been developed using an artificial neural network (ANN). The ANN model takes in six (6) inputs: evaporator water-side mass flow, its temperature, gas cooler water-side mass flow, its temperature, set-point output temperature, and high-side heat pump pressure. The model’s outputs comprise the electrical energy needed to run the heat pump, the heat from the gas coolers, the temperature of the heat pump-heated fluid, and the outlet temperature of the heat pump’s evaporator. Data used for training, validating, and testing the ANN model were generated by running a calibrated Modelica model of the CO2 heat pump for various combinations of input parameters obtained from Latin hypercube sampling. The ANN model developed includes an input layer with 6 inputs, 2 hidden dense layers, each with 30 neurons, and an output layer for 4 outputs (6-30-30-3). The ReLU activation function was implemented on each hidden layer and no regularizations were imposed. The Adam optimizer was used with a learning rate of 0.001 specified. Early stopping (patience = 2000) was implemented to ensure that the training data was not overfitted. A maximum of 30000 epochs was specified. The resulting Mean Square Error (MSE) obtained for the training, validation, and testing data sets were 1.38x10−5, 2.05x10−5, and 3.65x10−5, respectively. When tested against one-week operational runs generated by Modelica, the Root Mean Square Errors (RMSEs) for coefficient of performance (COP)s for spring, summer, autumn, and winter operations obtained were 0.232, 0.346, 0.089 and 0.076, respectively. The resulting surrogate ANN model can be integrated into the system model as a functional mock-up unit within Modelica to facilitate faster simulations for operational optimization.

Constraint-Based snowplow optimization model for winter maintenance operations

In the annual winter route maintenance, transportation agencies often deploy multiple fleets of trucks for snow control and removal activities over a vast maintenance area which creates an operational problem in determining the optimal maintenance routes and fleet size. The objective of this paper is to develop a snowplow routing optimization model to enhance the efficiency of snow removal route planning. The routing optimization model was developed using vehicle routing problems, constraint-based programming, and geographic information system. The developed model was applied to optimize the snow removal route planning practice of a District in Kansas, United States, as a case study. The result of this study shows that the optimization model can help minimize the fleet size and increase the level of service for treating snow routes within the selected District. The results of this study are expected to assist transportation agencies in optimizing their snow route removal in winter maintenance operations.

Investigation of Jamming Phenomenon in a Direct Reduction Furnace Pellet Feed System Using the Discrete Element Method

A continuous iron ore pellet feed system for a direct reduction ironmaking furnace is reportedly jamming in a hopper above the furnace, where a counterflowing gas seals off the furnace flue gas. The conditions that result in jamming are not well understood. The system is computationally modeled utilizing the coupled discrete element method (DEM) and computational fluid dynamics (CFD) technique. The technique is computationally expensive; therefore, the pellet sizing is modified while preserving the key metrics important in jamming. The model is used to study the impact of pellet moisture, heating, and ice formation between pellets in relation to the jamming event. The results indicate that the influence of moisture alone on the bulk shear rate is unlikely to jam the system and that insufficient heat is supplied by the counterflowing gas to raise the temperature of the pellets, which suggests freezing conditions can exist within the hopper. Particle bonding is implemented to replicate wet and icy pellets freezing and breaking up. The results indicate that the system jams in winter conditions when the hopper is charged with a minimum of 15% icy pellets, or 10% icy with 5% wet pellets. These results agree with industry reports of jamming during winter operations.

Mechanical, thermodynamical, and microstructural characterization of adhesion evolution in asphalt binder-aggregate interface subjected to calcium chloride deicer

Frigid weather and freezing road conditions in cold regions result in reduced pavement surface friction, compromised vehicle maneuverability, leading to deteriorated transportation safety. Given their effectiveness and low cost, using chloride-based deicing agents such as CaCl2 has become a standard winter maintenance strategy for many highway agencies. However, coupled with freeze–thaw (F-T) cycles, CaCl2 can adversely affect the durability of asphalt pavements and cause complex distresses such as moisture-induced damage, among others. Despite this fact, the mechanisms and the extent of the damage made to the asphalt binder-aggregate interface due to use of chloride salts are not well-understood. This study was undertaken to characterize the mechanisms by which different eutectic concentrations of aqueous CaCl2 solutions and F-T cycles affect the adhesion between asphalt binder and different aggregates through a multi-scale characterization and testing program. Given the rise in use of polymer-modified asphalt binder a PG 64–34 asphalt binder and aggregates of granite, quartzite, and limestone mineralogies were evaluated. More specifically, pull-off strength of the aggregate-binder systems subjected to different aqueous concentrations of CaCl2 and F-T cycles were determined. In addition, surface free energy method as a thermodynamic-based approach was employed to determine the effect of salt concentration on the adhesion and debonding energies, and the moisture-induced damage potential of the binder-aggregate interfaces. Furthermore, atomic force microscopy was employed to evaluate the effect of salt concentration and F-T cycles on surface morphology, nanostructure, and adhesion of the asphalt binder. The applied multi-scale characterization provided invaluable information about the physio-chemical phenomena responsible for the accelerated damage in asphalt binder-aggregate interface when subjected to different aqueous concentrations of CaCl2. Gaining an accurate understanding of the damage mechanism is necessary for developing new materials and methods for combatting salt-accelerated moisture-induced damage in cold regions as a result of winter maintenance operations.

Feasibility assessment of self-deicing concrete pavements with recycled CFRP reinforcement: Environmental and mechanical performance

Electrically conductive concrete (ECC) with carbonaceous conductive elements has been studied for self-deicing pavements; however, carbon fiber production is costly and resource-intensive. Therefore, mechanically recycled carbon fiber reinforced polymer (CFRP) scraps from plane manufacturing were evaluated as reinforcing and conductive elements for ECC.Evaluation of mechanical properties showed that 5%Vol rCFRP-ECC had approximately 20% higher flexural strength and twofold greater absorbed fracture energy than the reference plain concrete. In a -15 °C environment, this ECC melted ice at a heating rate of 0.05 °C/min and a 3.8 kWh/m2 power consumption.A life cycle assessment modeling of winter maintenance operations for one snowstorm in the Sierra Nevada Mountain regions in California showed that rCFRP-ECC and two other ECCs from the literature have a significantly higher global warming potential from energy consumption than snowplowing, sanding, and salting. Nevertheless, important advantages of ECCs in reducing chloride contamination in water and soil, corrosion in vehicles and infrastructure, and stronger and more durable fiber-reinforced infrastructure materials should be considered in decision-making.

Development of an Autonomous Measurement System for Estimation of Snow Profile on Road Surfaces

This paper presents the development of a novel prototype system to measure the profile of snow and detect ice on road surfaces. While researchers have previously developed methods to estimate the tire-road friction coefficient, methods to create a high resolution map of snow cover and ice on roads have not been developed. The prototype and data processing methods presented in this study provide a potential solution that would greatly benefit winter road maintenance teams. Having a device capable of creating a detailed map of snow and ice cover on roads would allow for more efficient plowing and salt deployment, better informing of the public about current road conditions, as well as better analysis of current plowing techniques to improve winter road maintenance operations. The measurement of the snow profile is based on the use of two types of inexpensive ranging sensors mounted on a rotationally controlled stepper motor platform. The technical challenges addressed in the development include analysis of the scan path over which the system scans the road, differentiation between asphalt, concrete, and snow surfaces using reflection amplitudes, accurate transformation of raw distance measurements to inertial coordinates, and identification of the Euler angles of the sensors using parameters associated with the scanned surface profile. Extensive experimental results and test data are presented, which show that the system is capable of measuring the snow profile on a road with an accuracy of ±1 cm when a reasonable portion of bare road is visible, and ±2 cm when only small portions of bare road are visible.

Enhancing Winter Road Maintenance via Cloud Computing

In this study, we developed an AI-enhanced cloud computing framework to enable the autonomous decision-making quality and precision of winter road operations for connected living, namely Smart Maintenance Decision Support System (SmartMDSS). With the support from the Federal Highway Administration (FHWA) and Michigan Department of Transportation, we are among the first to develop AI-enabled tools for practical winter road maintenance decision-making. On the front end, SmartMDSS provides a user-friendly graphical interface showing all the valuable data and winter operations for specific points on the road and sends necessary warnings and notifications. On the back end, SmartMDSS extracts and analyzes data and makes winter road maintenance decisions. Convolutional Neural Network (CNN) has been successfully employed to identify the snow coverage on the road surface. Decision-making algorithms were proposed and implemented to support the road engineers and operators for real-time winter maintenance operations.

Water Quality and Flow Management Scenarios in the Qu’Appelle River–Reservoir System Using Loosely Coupled WASP and CE-QUAL-W2 Models

The water bodies of the Saskatchewan Prairies suffer multiple stressors, and demand for water is expected to increase. Water quality models can help evaluate water management strategies and risks such as climate change. This study assesses the impact of interbasin water transfers on the water quality of a strategic, eutrophic prairie reservoir that receives poor-quality watershed run-off. A one-dimensional WASP model was used to estimate nutrient transformations in the transfers along a 97 km river channel. The WASP model was then loosely coupled to a two-dimensional CE-QUAL-W2 model of the downstream receiving reservoir. Output from the WASP model was manually transformed into boundary conditions for the CE-QUAL-W2 reservoir model. This method improves on an earlier attempt to estimate nutrient transformations in the transfers using linear regression. Results from the loosely coupled models suggest Buffalo Pound Lake would respond well to the interbasin transfers. The number of threshold exceedances decreased for all modeled water quality variables. Nutrient concentrations were most influenced in the open water season following spring freshet. Any additional reduction of threshold exceedances during winter was minimal in comparison. These results are interesting from a management perspective as increased transfers under winter operations risk ice damage to the river channel.

Development of method for anti-icing materials consumption optimization during winter road maintenance

The object of research is the processes of managing the consumption of anti-icing materials during the winter maintenance of highways. The main hypothesis of the research is the application of mathematical modelling methods to optimize the consumption of anti-icing materials. The study considered the factors that affect the use of both individual types and anti-icing materials as a whole. It was determined that the temperature of the road surface and the selected winter maintenance strategy taking into account the level of service (LOS) are significant factors. A mathematical model of material consumption optimization based on the binary numbering system is proposed. Additional limitations to the model have been established: compliance with the criteria of reliability, preservation of assets, road safety, compliance with user expectations (social factor), environmental friendliness, efficiency of the use of materials. An algorithm for the application of the method of optimizing the consumption of anti-icing materials during the winter maintenance of highways has been developed. It was determined that the developed method solves the main problem of minimizing the costs of anti-icing materials, taking into account the requirements of the level of service, the chosen strategy, the temperature of the road surface and the criteria of impact on winter maintenance. It is proposed to implement the calculation according to this method using the built-in «Parameter selection» function in the Microsoft Excel license environment (USA). The results obtained by the optimization method, based on real data, showed the possibility of saving materials by about 23 %, which confirms the effectiveness of the proposed solutions. The results of calculations based on the method of optimization of material consumption have practical value and can be used to make management decisions regarding the justification of winter road maintenance operations. The proposed method can be used both for the cost planning phase and in the case of analysis in the post-implementation period. This is an important aspect in the conditions of financial and resource uncertainty, including those related to circumstances of force majeure, in particular, martial law in Ukraine.

Climate change impacts the state of winter roads connecting indigenous communities: Case study of Sakha (Yakutia) Republic

Winter roads, or zimnik, serve as major connections between communities across the global Arctic, including Sakha (Yakutia) Republic. Although accessible to general public, winter roads in remote regions are primarily used by indigenous communities. Sustainability of winter roads is reduced by climate change effects, via shorter and milder winters, extended shoulder seasons, delayed freeze up and advanced ice break up on rivers used as ice roads. We review the observed and projected change in mean monthly air temperatures, MMAT, °C, during cold season in six localities of the Sakha (Yakutia) Republic, important residence areas of the indigenous peoples of the North. In observed MMAT records, only North-Western Yakutia hasn’t experienced significant warming. In other localities, a significant step-shift change is observed in months from March to June, and at several stations, also in October and November. Under future climate, assessed with a regional ensemble of global climate models, projected change is expected in core winter months, November to February. In the near future, 2021–2050 period, increase in MMAT is expected mostly in December and January, with only minor increase in shoulder seasons, except in southern Yakutia. In the far future, 2071–2100, only under optimistic SSP 1–2.6 scenario the MMAT change is contained within +3.5 °C, and even in this case, April MMAT increases above −2°C at stations in southern Yakutia. Under SSP 5–8.5 scenario, highest MMAT increase, up to over +12 °C, is projected in the Yakutian Arctic from December to February. In southern Yakutia, both October and April MMAT around or above 0 °C are projected. Winter Road Sustainability Index is assessed based on observed and projected climate. Over northern Yakutia, higher MMAT in core winter months suggest reduced ice thickness on rivers, but overall climate severity allows sustainable winter road operations throughout the season even under high emission scenarios. In the near future, only winter road operations around Tyanya, in the Evenki residence area, become moderately affected under most SSP scenarios. In the far future, winter road operations around Tyanya, Ust-Maya and Neryungri, also Evenki residence area, become highly vulnerable under most scenarios. Practical implications include institutional response, transport system adaptation, adjustment of road maintenance protocols, and reconsidering local production.

Advanced thermal and rheological characterization of a sustainable anti-icing hydrogel for winter road maintenance operations

As a part of the advancements in the winter road maintenance, a sustainable anti-icing hydrogel has been recently developed for the treatment of the open-graded asphalt pavements. It is obtained by mixing water, a chemical agent and a phyto-based additive (seaweed fiber powder) having thickening and gelling properties. The product has the ability to form a gel-like structure when hot-sprayed on a cold pavement surface. The influence of the interaction between the solutes, i.e. sodium chloride (marine salt) and seaweed fiber, on the gel behavior was evaluated through a laboratory scale study, based on thermal (differential scanning calorimetry) and rheological (rotational and oscillatory tests) analyses. The research findings offer a substantial support for the definition of most suitable recipe and the configuration of the spray application system. The results revealed that the formulation of the anti-icing hydrogel is predominantly governed by the salt concentration, which establishes the freezing point depression capacity. Once the salt dosage is defined on the basis of the climatic condition and the accepted level of service, the gellant content, which controls the gelation mechanism, can be optimized to obtain a sprayable gel with a sufficient stiffness and strength.

Comparison of anthracite and GAC biofilter performance for surface‐water manganese removal

AbstractAlameda County Water District has observed increased biofilter effluent manganese concentrations during winter operations. To investigate manganese removal across surface water biofilters during cold‐water conditions, trends were analyzed between water temperature and manganese removal across multiple testing scales, multiple biofilter influent water qualities, and both anthracite and GAC media. During acclimation of new biofilters, 100% removal of manganese was observed sooner across both anthracite and GAC biofilters brought online at 20°C compared to 12°C. Acclimation at 12°C required 18 extra days for the GAC biofilters and 48 additional days for the anthracite biofilters. For fully acclimated biofilters, a decrease in manganese removal across both GAC and anthracite biofilters at temperatures below 15°C was observed. However, greater and more consistent manganese removal was observed across GAC compared to anthracite biofilters. Performance differences between locations also suggest that operational and water quality conditions also likely affect manganese removal.

Crushing effects on the durability of rocky aggregates used on road surfaces subjected to winter maintenance and extreme climate conditions

• The evolution of the internal structure of rocks regarding their crushing and sizing processes has been analyzed. • The effects of freeze-thaw and thermal stress durability tests with NaCl has been also studied. • Both rocks lost similarly weight and increased their water absorption coefficients after crushing and durability tests. • Durability tests affected more to aggregates than rocks, due to the negative impact of the crushing processes. • The specific area subjected to the agents of deterioration in the durability tests should be consider in future studies. • Thermal stress with salt is the most damaging durability test performed, especially on the aggregates analyzed. The internal structure of a rock is determined by its empty spaces (pores and cracks), which can evolve over time by chemical, physical, or biological processes. In construction, these chemical and physical processes, and their effects in the final products, known as aggregates, are well known, and controlled to estimate their quality and durability. However, the intrinsic properties of the raw material, the rock, and the processes that can affect its quality and durability during the production of the aggregates are poorly studied and considered in construction. For this reason, the evolution of the internal structure of two different types of aggregates used in Spanish roads were analyzed. The aggregates will be also studied in their raw form, rock, by measuring their water absorption coefficient and using mercury porosimetry and fluorescence microscopy. To estimate the influence of the internal structure in the durability of the aggregates, both forms, rocks, and aggregates, will be subjected to the combined effect of temperature variations and the salt used during winter maintenance operations on Spanish roads. The results obtained show that crushing processes in the quarry increase the water absorption coefficient of both materials and generate new micro-porosity caused by the microcracking of weak areas of the rock such as its schistosity or the presence of previous cracks. Thus, this increasing of the porosity produced by crushing processes in the quarry has a negative impact on the durability of the aggregates when they are subjected to winter maintenance operations and extreme climate conditions.