Seasonal Load Restrictions Research Articles

Comprehensive in-situ freeze-thaw monitoring under a granular-surfaced road system

Annual freeze-thaw cycles significantly impact the performance of granular-surfaced roadways in cold regions. Various methods are employed to limit the damage caused by these events. A seasonal load restriction (SLR) is one of these methods that is imposed by local roads agencies according to the subgrade condition. Subsurface monitoring is a reliable method to help such agencies determine the status and freeze-thaw behavior of the subgrade in real-time. To help improve the capabilities for predicting the depths and timing of soil freeze-thaw events, a comprehensive subgrade sensor network was installed below a granular-surfaced roadway in Hamilton County, Iowa. The network continuously measured temperature, volumetric water content and matric potential at selected depths below the subgrade surface, and also recorded atmospheric conditions using a weather station installed next to the instrumented roadway section. In this study, the collected dataset for the winter 2019–2020 freeze-thaw period was analyzed using contour mapping over the entire road cross-section. The results of the study revealed unsymmetrical behavior under the road surface. The freezing front was found to be deeper towards the west midpoint and shallower at the shoulders. Volumetric water content and matric potential measurements were also found to be coherent with the temperature data during freezing and thawing periods. In addition, soil water retention curves of matric potential versus water content determined in the laboratory were compared to the curves derived from the field measurements. The two datasets were found to be quite different due to both predictable and unpredictable field conditions.

Potential Use of NASA’s Soil Moisture Active Passive Freeze–Thaw Tool to Assist in Minnesota Seasonal Load Restriction Timing

The U.S. encompasses over 4 million roadway miles, with about half of them located in seasonal frost areas. Roads are especially susceptible to damage when the subsurface is saturated with water (i.e., spring thaw). Spring load restrictions (SLR) are important for maintaining the integrity of roads. Routine determinations of road freeze–thaw (FT) state are limited to vertically embedded temperature data probes (TDP). Although TDPs are valuable to departments of transportation for determining SLRs, TDPs only represent individual points within the road network and are costly to install and maintain. Recent updates to spaceborne technology and algorithms made physically based retrievals of FT conditions possible at improved accuracy and temporal resolution (every 3 days) and have potential use for assisting with SLRs. Although instruments such as NASA’s Soil Moisture Active Passive (SMAP) platform cannot resolve individual roads, past comparisons have shown good correspondence with TDPs. The main objective of this study is to provide information on the potential value of NASA’s SMAP FT tool to supplement other methods for making seasonal load restriction decisions. Results are compared against data and protocols by the Minnesota Department of Transportation (MnDOT) at 10 sites in Minnesota, over four winter seasons (2016–2019). Results show that even when a simple criterion is used—the date of the third consecutive thaw from the SMAP afternoon retrieval—those dates typically fell within a week of MnDOT road postings (61% of the time). In addition, SMAP FT typically matches TDP FT states throughout the year (79% of the time).

Remotely Sensed Freeze-Thaw from the Soil Moisture Active Passive Instrument to Inform the Timing of Seasonal Load Restrictions in Alaska

To balance the competing interests of minimizing road damage while maximizing economic opportunities on low-volume roadways located in seasonal frost areas, road management agencies typically apply spring load restrictions (SLR). The Alaska Department of Transportation and Public Facilities (ADOT&PF) uses an observational approach to determine SLR, which utilizes temperature data probes (TDP). Although their TDP network is fairly extensive, comprehensive coverage of all roads within the ADOT&PF domain is not feasible. Passive microwave spaceborne freeze and thaw (FT) observations may be useful for bridging these observational gaps. This study compared satellite FT retrievals with TDP data at 15 sites in Alaska to determine whether satellite-retrieved FT can inform on road FT; whether there are substantial morning (AM) and afternoon (PM) satellite-retrieved FT differences; and assess how Soil Moisture Active Passive (SMAP)-based SLR application dates compare with ADOT&PF SLR-applied dates. Our evaluation showed that SMAP AM (PM) FT retrievals are highly representative of FT in roads with an overall accuracy of 79% (83%). SMAP-retrieved FT states corresponded to individual TDP locations 52–95% of the time, and FT accuracy exceeded 80% at 9 (11) out of 15 TDP sites for AM (PM) data. SMAP thaw detection was very consistent with respect to road data: (1) interannual variation of FT accuracy at individual TDP sites was low (16%) and (2) strong SMAP thaw signals were detected about 10-days ahead of all roads having thawed according to TDP data.

Climate change: potential impacts on frost–thaw conditions and seasonal load restriction timing for low-volume roadways

Low-volume roads constitute a major percentage of roadways around the world. Many of these are located in seasonal frost areas where agencies increase and decrease the allowable weight limits based on seasonal fluctuations in the load carrying capacity of the roadway due to freeze–thaw conditions. As temperatures shift due to changing climate, the timing and duration of winter freeze and spring thaw periods are likely to change, potentially causing significant impacts to local industry and economies. In this study, an ensemble of 19 climate models were used to project future temperature changes and the impact of these changes on the frost depth and timing of seasonal load changes across five instrumented pavement sites in New England. The study shows that shifts of up to 2 weeks are projected at the end of the century and that moderate variability was observed across the study region, indicating that local conditions are important for future assessments depending on the desired level of accuracy. From 1970 to 1999, the average freezing season lasted between 9 and 13 weeks in the study region. By 2000–2029, the frozen period shortens by approximately 10 days over baseline duration (10–20% reduction). By the end of the century under RCP 4.5, frozen periods are typically shorter by 4 weeks or a 30–40% reduction. However, RCP 8.5 results indicate that four out of the five sites would have no frozen period during at least six winters from 2060 to 2089.

Enhancements to a Simple Pavement Frost Model

The ability to predict the timing of thaw is important to the design and the maintenance of roads. Such knowledge allows highway agencies to reduce the damage caused by enacting seasonal load restrictions during thawing. Because measurement by observation or testing requires time and resources, one desirable solution is a predictive model. Simple models, such as the Cornell Pavement Frost Model (CPFM), require only daily temperature weather inputs. Possible improvements to the CPFM were tried with a primary goal of improving thaw timing while keeping the model simple and applicable across New York State and beyond. The proposed modifications included changing the thaw reference temperature, providing better tracking of thermal properties throughout seasonal changes, and driving thaw from underneath with a constant underground temperature. During the investigation, further changes were tested involving ways to treat latent heat during thaw, including decoupling. Changes were tested against frost tube data from Ithaca, New York. The model was evaluated and adjusted at other sites in Maine and New Hampshire. The additional inputs required by the resulting modified model were depth and value of constant temperature, moisture content, and estimated shade. The modified model greatly improved the depth and timing of the model for most sites. The final model used the original formulation with minor modifications for freezing and a latent heat formulation for thawing. With more than one freezing–thawing front, the formulas were decoupled. Further investigation should be done to improve frost depth and timing, and in particular, to find out why rapid thawing from underneath does not occur at some sites.

Modification of the U.S. Army Corps of Engineers Model 158 for Prediction of Frost–Thaw Profiles in Northern New England

Knowledge concerning frost–thaw profiles is important in the design of pavements for low-volume roads located in seasonal frost areas. In addition, because these roads are highly susceptible to damage during the spring thaw–weakened period, this knowledge is helpful in making decisions about seasonal load restriction (SLR) policies. Direct measurement of frost depth is expensive and usually limited in scope. Therefore, several highway agencies have expressed the need for a prediction model to help them in estimating frost–thaw profiles beneath roadways. A report from the U.S. Army Corps of Engineers New England Division examined frost prediction models. One of the equations in that report was Model 158, which was similar to the modified Berggren equation. Model 158 uses air temperature indexes as well as pavement material properties to integrate heat flow into the calculation of maximum seasonal frost depth. That model has been modified slightly and programmed in an Excel spreadsheet to predict frost and thaw depths on a daily basis. This paper presents an overview of the modified Model 158 and compares predictions obtained from that model with measured frost–thaw profiles obtained at several test sites located in northern New England during a period of five freeze–thaw seasons. Results suggest that the model shows much promise although it generally tended to slightly underpredict maximum frost depths. In tracking the thaw process for SLR posting, the model tended to be conservative in estimating end-of-thaw dates (especially during rapid thawing events); in many instances, estimated end-of-thaw dates fell after measured dates.

Implementation of Decision Support System (DSS) through the Integration of Commercial Remote Sensing (CRS), Model-View-Controller (MVC) Architecture and Object-Relational Mapping (ORM)

order to aid in the processes of data collection, interpolation, analysis, and predictive model development the integration of commercial remote sensing and spatial information technologies (CRS&SI) with object oriented modeling (OOM) using the model view controller architecture (MVC) has been applied. A Decision Support System (DSS) application that uses CRS&SI for collecting atmospheric, subsurface, and predicted weather data, is used to compare, interpolate, measure, predict, and determines the depth of frost and thaw penetration into the subsurface of roadways across New England, This process is used in order to guide State Departments of Transportation (DOT) in determining when to impose Seasonal Load Restrictions (SLR). Seasonal load restrictions limit the travel of heavy trucks on certain roads such that state DOT's can limit the damage to the roadway surface. The implemented DSS consists of a web based front end graphical user interface (GUI) that leverages popular web based technologies, programming interfaces, data collection scripts, data evaluation scripts, data interpolation scripts, predictive modeling, and a centralized database. The database was developed for storing newly collected, historic, and predicted data. This paper includes the description of the general purpose and use of the system and a discussion regarding the architecture, individual components, interactions, and a description of the sequence of events for data collection, processing, prediction, and interpolation.

Development of Frost and Thaw Depth Predictors for Decision Making about Variable Load Restrictions

Low-volume roads covering the northern part of Ontario, Canada, are a critical asset; they enable the movement of goods from remote resource areas to markets. However, challenged by a combination of heavy, low-frequency traffic loading and a high number of freeze-thaw cycles for which most have not been structurally designed, such highways often experience seasonal damage and premature traffic-induced deterioration. To mitigate these impacts, the Ontario Ministry of Transportation and other departments of transportation place seasonal load restrictions (SLRs) every year during the spring thaw. For economic reasons, the duration of SLRs is usually fixed in advance and is not applied according to conditions in a particular year. Rigidity in the schedule may result in economic losses because the payload can be unnecessarily restricted or pavement deterioration can occur. The latest attempts to address this issue include the use of climatic and deflection data to assess the bearing capacity of the roadway better. The use of frost and thaw depth predictors to track spring thaw weakening could improve the scheduling of load restrictions. On the basis of field data captured in Northern Ontario, a good correlation was found between the amount of frost depth in the pavement and weather conditions monitored by road weather information systems. An empirical methodology for site-specific calibration of the predictors is proposed, and the steps toward its development and the calculation algorithms are detailed.

Determining When to Place and Remove Spring Load Restrictions on Low-Volume Roads

Damage to low-volume roads in seasonal frost areas can be kept to a minimum by implementing seasonal load restrictions (SLRs). However, not all agencies responsible for road management implement such restrictions, and among those that do, there is no standard technique. Some use qualitative methods, such as observation or dates. Others use quantitative methods, such as measuring or estimating stiffness or subsurface temperature. Loss and recovery of summertime pavement strengths can be directly measured with a falling-weight deflectometer (FWD). Alternatively, mathematical models of varying levels of complexity, using meteorological data, are in existence or are undergoing development for SLR placement. However, initial investment for an FWD can be prohibitive for agencies such as the U.S. Department of Agriculture Forest Service (FS), for which road management is not a primary mission. Similarly, such agencies have limited personnel to conduct detailed calculations required of complex mathematical models. Consequently, easy-to-use, low-cost alternatives are needed. In cooperation with other partnering agencies, FS has recently been evaluating several techniques for determining when to place and remove SLRs. Three methods that appear promising include (a) subsurface instrumentation for temperature and moisture, (b) portable or lightweight FWDs, and (c) the thaw index. Technology-and-development efforts in each of these areas are outlined here; field test programs, observations, and analysis procedures are discussed; and recommendations for implementing each method are provided.

Lifting All Seasonal Load-Restrictions in Norway in 1995 Background and Effects

To preserve invested capital and to protect the existing road network against excessive damage during thaw periods, the Norwegian Road Authority adopted extensive axle load restrictions on both national and county roads up to 1995. On January 1, 1995, all axle load restrictions during the spring thaw period within national and county road networks were lifted after calculations indicating that the total efficiency gain after an administrative lifting would be considerable, provided it was followed by extra funds to maintain the surfacing serviceability. However, without such extra funds, a lifting of restrictions would result in reduced surfacing serviceability and considerably increased cost for all road users. As of 2003, there are no indications that the lifting of load restrictions in 1995 has resulted in reduced road surfacing serviceability. This is remarkable, as the annual budgets for resurfacing during the same period have been considerably reduced. One or more of the following items can explain why it has been possible to maintain the road surfacing serviceability at the 1994 level during a period with reduced budgets: road users have not fully utilized the new possibilities in higher axle loads; there has been a slow but steady increase in surfacing service life from 1985 to 2002 from 10 to 15 years for national roads and from 13 to 18 years for county roads; there has been a reduced use of studded tires during winter; and there has been a general development of asphalt techniques and procedures, such as thin surfacings.

Parks Highway Load Restriction Field Data Analysis: Case Study

The loss of pavement strength during spring thaw could result in excessive road damage under applied traffic loads. Damage assessment associated with the critical thaw period is essential to evaluate current load restriction policies. The Alaska Department of Transportation and Public Facilities proposed a plan that will provide an engineering analysis of field conditions with 100-percent loads on the Parks Highway for 1996. Extensive data were collected and analyzed in an effort to monitor pavement damage during the spring of 1996 and to determine loss of pavement strength. Field data included truck traffic data from scalehouse and weigh-in-motion (WIM) stations, pavement temperature data, profilometer data for roughness and rutting, and falling weight deflectometer data. Analyses were performed to compare WIM and scalehouse traffic data and to determine the fraction of overweight axle-loads and corresponding pavement damage during spring thaw. Northbound and southbound truck traffic and its effect on pavement damage were considered. Ground temperature measurements were analyzed to determine when thaw initiates and how long seasonal load restrictions are required. In addition, comparisons of remaining life with and without load restrictions using mechanistic methods were conducted.