Spring Thaw Predictor and Development of Real Time Spring Load Restrictions

Eaton, Robert A.; Hanscom, Alan; Kestler, Maureen A.; Hall, Andrew; Miller, Heather J.; Berg, Richard L.

doi:10.1061/41072(359)54

Cited by 9 publications

(8 citation statements)

References 1 publication

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“…With this, hardware equipment is typically not connected to any network for remote collection. This displays a clear calling for remote sensing, as concluded in technical roadway reports [5,6,7,8] and published studies [9,10,11,12] These studies also outline the manual collection, and the a manual interpolation of the data that currently occurs, as well as a manual comparison of the sensed values. These tasks must occur on the collected data from the non-networked devices.…”

Section: Previous Systemmentioning

confidence: 88%

“…Studies show that more refined imposition and removal of load restrictions can help limit damage to roadway surfaces. [9,10,11,12] Instances where data collection, processing, and interpolation have occurred have historically been disjoint and performed manually, sometimes days or weeks after visiting the roadside site. This delay leads to inaccurate restriction time frames, sometimes ranging in weeks.…”

Section: Previous Systemmentioning

confidence: 99%

“…Regions that are majorly affected by these seasonal restrictions include New England and other northern regions throughout the country, including Minnesota [1,2], Washington [13], New Hampshire, and Maine. [5,6] In this system, records of sensed atmospheric data, predicted atmospheric data, and subsurface data are collected. Calculations are executed for data interpolations, calculations, and prediction algorithms for support within the system.…”

Section: Introductionmentioning

confidence: 99%

“…This manual process of data collection is tedious and delayed due to large distances between processing centers and collection sites. [9,10,11,12] This is one of the causes for over-estimation of when SLR should be imposed, adding to inconvenience and cost for longer periods of time. This research project eliminates manual data collection replacing it with a multi-part system using (1) commercial remote sensing and spatial information (CRS&SI) technologies utilizing satellite networks for data transfer and information staging, In order to begin drafting and designing the developed system, research extended into topics including CRS&SI for data collection at various global sites, ORM for modeling of data and manipulations of data within the system, and the use of a lightweight MVC web based application interacting with objects generated using the ORM library.…”

Section: Introductionmentioning

confidence: 99%

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

O'connor¹,

Balasubramanian²

2013

IJCA

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In 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. General TermsCommercial Remote Sensing and spatial information technologies (CRS&SI), Model View Controller Architecture (MVC), Object-Relational Mapping (ORM) Keywords commercial remote sensing, decision support system, seasonal load restrictions, frost-thaw predictive model, and satellite data collection, model view controller, object relational mapping 32 Fig 3: Many tables in the database represent entities (objects), as shown in this partial view of the preliminary ER diagram. Some entities include states, establishments, sites, data loggers, sensors, sensor readings, and sensor types while other tableswere created simply to house calculated data or track service import schedules.

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Section: Previous Systemmentioning

confidence: 88%

Section: Previous Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

O'connor¹,

Balasubramanian²

2013

IJCA

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“…Currently, tedious labor is required to collect the sensor data and to transform it into the right format for processing and visualization [4,5]. The research described in this paper includes a seamless visualization of the data uploaded into our database of a web-invoked system.…”

Section: Introductionmentioning

confidence: 99%

Seamless data visualization for frost detection

Huang

Azogu

Sunkara

et al. 2011

2011 7th International Wireless Communications and Mobile Computing Conference

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Multimedia networking has been expanding its definition beyond communications of text, image, audio, and video as the Next Generation Internet evolves from social networks to cyber-physical networks. One application related to transportation infrastructure includes health monitoring of existing paved and un-surfaced roads. Embedding remote sensing and spatial information technology into roadways facilitates constant observation of road conditions and automatic detection of frost and thaw fronts during the spring thaw and recovery period. A system is being developed which provides critical quantitative data to eliminate or supplement components of current visual inspection procedures, and thus greatly assists transportation agencies in making spring load restriction (SLR) placement and removal decisions. This paper presents the data visualization module of a Decision Support System for Spring Load Restriction (DSS-SLR). After temperature data are collected by underground sensors and transferred by wireless/wired networks to a central database, a user can view the spatial and temporal temperature patterns via a Web browser. An embedded interpolation routine and a graphical user interface (GUI) enable the user to view a cross section showing frost and thawdepths over time. Our work pioneers this new frontier of multimedia networking, which will have significant applications with regard to health monitoring of existing paved roadway systems, as well as un-surfaced road evaluation and maintenance.

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Hydraulic Conductivity of Cement-Treated Soils and Aggregates after Freezing

Shea

2012

Cold Regions Engineering 2012

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Improvements in the strength and durability of frost-susceptible soils and aggregates can be achieved through chemical stabilization using portland cement, where the efficacy of cement stabilization for improving durability depends on the degree to which hydraulic conductivity is reduced. Hydraulic conductivity is commonly estimated from basic soil properties using Moulton's empirical equation. However, the hydraulic conductivity estimation does not consider the detrimental effects of freezing or the benefits of cement stabilization. The purpose of this research was to derive new equations relating hydraulic conductivity after freezing to specific material properties of cement-treated soils and aggregates stabilized with different concentrations of cement.

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Spring Thaw Predictor and Development of Real Time Spring Load Restrictions

Cited by 9 publications

References 1 publication

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

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

Seamless data visualization for frost detection

Hydraulic Conductivity of Cement-Treated Soils and Aggregates after Freezing

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