2023

Erdem Balcı, Istanbul Universitesi, Cerrahpasa
Niyazi Özgür Bezgin, Istanbul Universitesi, Cerrahpasa

Abrupt changes in profile may exist at particular locations along a railway track such as bolted and welded rail joints, or at localised defects developed on the rail due to wheel forces over time. Variations in the track profile can cause development of dynamic impact forces that may lead to deterioration of track elements and higher maintenance costs. The analysis of a wheel passing over a track irregularity with an abrupt difference in profile requires complex numerical modelling or sophisticated track instrumentation to collect real-time data. However, researchers also need an analytical tool that they can use to conduct an explicit analysis for the dynamic impact forces that arise due to such abrupt variations in profile. There is a need for a reliable and a practical approach for the estimation of dynamic impact forces due to track and wheel roughness. This paper proposes the use of an analytical equation previously developed by the Bezgin Method to estimate the highest values of the dynamic impact forces due to a descending track profile for analysing the effects of abrupt changes in track profile at rail ends and compares the analytical estimates with the estimates of existing empirical equations and experimental measurements.

Niyazi Özgür Bezgin, Istanbul Universitesi, Cerrahpasa
Mohamed Wehbi, Network Rail
Erdem Balcı, Istanbul Universitesi, Cerrahpasa

Variations in railway track profiles can increase the vertical forces imposed unto them by train wheels. Rail profile can vary rapidly within turnouts and generate high dynamic impact forces on the track. Increased wheel forces on the track can damage the rail, the rail bearing elements and the supporting layers of the track along with the wheels and the wheel bearing elements of the trains. Railhead and wheel thread plastification, fracture and rolling contact fatigue, rail seat fracture, ballast pulverization, and cumulative track settlement due to increased stresses on ballast and subgrade are some of the damages caused by increased wheel forces due to rapid changes in track elevation. This paper demonstrates the application of an analytical method recently introduced to the railway engineering literature, known as the Bezgin Method to estimate the wheel impact forces generated due to track and wheel roughness and an assessment of the consequences of these forces on ballasted railway tracks through case studies.

Qian Fu, University of Birmingham
John Easton, University of Birmingham
Michael Burrow, University of Birmingham
James Sweeney, Network Rail Ltd

The current measures for the railway track fixity in the UK’s railway system remain at a relatively low level of granularity. This paper presents a pilot study of the development of an integrated computing framework for improving the measurement, prediction and analysis of profile-specific fixity of railway tracks in the context of the UK rail network. The framework is aimed to produce a data integration and mining tool, which can determine track fixity parameters for any given section of track. In this fundamental phase of the study, we propose to measure the track movement based on LiDAR point cloud data and describe the track fixity by a set of parameters, which are associated with the direction of track movement relative to the plane of rail and the rate of the movement within a certain period. We seek to integrate a data mining algorithm into the framework to predict the values of those parameters, given a very large amount of heterogeneous data in the area. From the pilot study, a prototype framework, which allows the rapid implementation of data workflows with the functionality, has been created. We demonstrate the feasibility of the prototype by training a random forest model on the real data from an 80-km section of the East Coast Main Line south of Edinburgh in Scotland. Curvature, cant, and maximum speed of trains proved to be the key factors that impact on, and hence are critical for predicting and analyzing, profile-specific track fixity.

Md. Fazle Rabbi, Oklahoma State University
Rakan Alturk, ENSCO, Inc.
Radim Bruzek, ENSCO, Inc.
Theodore Sussmann, OST-R/Volpe Center
Hugh Thompson, Federal Railroad Administration (FRA)
Debakanta Mishra, Oklahoma State University

Strain gauges are often used to measure vertical wheel loads in a railroad track. This approach is based on the concept of Differential Shear Strain (DSS) measurement: the difference in vertical shear force between two points along a beam equals the magnitude of the resultant of applied vertical forces in between. With a slight modification to the strain gauge positions and installation of an additional set of strain gauges, this concept can be extended to measure the vertical rail-tie interface reaction forces, thus quantifying tie support conditions. Although the application of differential shear strain measurements for vertical wheel load quantification is widely prevalent in the railroad community, the validity of this approach for tie reaction measurement has been relatively unexplored. Conceptually, the approach is identical to the vertical wheel load measurement system, with the only difference being the placement of the strain gauges along the rail. Nevertheless, several questions have been raised regarding how different track and loading configurations can affect the accuracy of such a measurement system. To address some of these concerns and establish this approach as a viable method for tie support condition assessment, a field validation effort was recently undertaken. Under the scope of this collaborative effort, the strain gauge-based DSS measurement system for rail-tie interface reaction force measurements was evaluated in the field under a static and dynamic loading. The study showed that the strain gauge-based measurement approach is as suitable as some of the conventional methods of tie reaction force measurement.

Erol Tutumluer, University of Illinois, Urbana-Champaign
Zhongyi Liu, University of Illinois, Urbana-Champaign
Bin Feng, University of Illinois, Urbana-Champaign

Train-track interaction models are widely used for simulating dynamic responses of train and track components. In a conventional train-track analytical model, ballast layer is often simplified as mass blocks and interacts with other components through a spring and dashpot system. Such an idealization ignores the particle level information, i.e. interaction of different sized and shaped aggregates and related degradation characteristics linked to fouling behavior of the ballast layer. On the other hand, realistic ballast models based on the Discrete Element Method (DEM) can capture the particle level information, but require predefined external loading patterns as inputs to mimic the train passages. To overcome such drawbacks of train-track and DEM models, this paper proposes to couple the two calibrated models together to build a more realistic ballasted track model for predicting dynamic responses of the train and track. The coupled model was first validated with detailed field data collected from Amtrak’s Northeast Corridor. The validated model was then utilized to study the effects of crosstie spacing realizing that a smaller crosstie spacing than regular often results in a higher construction cost. Increasing crosstie spacing, however, was found to result in larger track displacements, crosstie accelerations and reaction forces, particle accelerations and local average normal contact forces. Therefore, vibration patterns with a smaller crosstie spacing were more stable. Such observations suggest that crosstie spacing plays an essential role in controlling track dynamic responses, and an optimum crosstie spacing could be determined by utilizing the newly introduced coupled model.

Babak Asadi, University of Illinois, Urbana-Champaign
Ramez Hajj, University of Illinois, Urbana-Champaign

Under Tie Pads (UTPs) have been a subject of increasing interest in railroad track engineering for mitigation of vibration in the trackbed and reduction of damage to the ballast caused by concrete crossties. Despite showing promise in the existing literature, UTPs are expensive relative to other track components. This paper focuses on recent efforts to develop a low-cost alternative to UTPs made of asphalt concrete and recycled crumb rubber. Four initial mixes were developed using a single optimum gradation and various asphalt and rubber contents. After initial determination of the bedding modulus of these materials, an optimum mix design was selected, and the research team conducted a long-term fatigue test using a Geometric Ballast Plate (GBP). Results indicated that more than 94% of the bedding modulus was retained after 2.7 million cycles, and no visible or material degradation of the RAC specimen was observed. It is therefore suggested that RAC could serve as a viable alternative to UTPs under concrete crossties as a customized material for specific track applications in the field.

Tzu-Hao Yan, Swiss Federal Institute of Technology (ETH Zurich)
Mariana De Almeida Costa, Swiss Federal Institute of Technology (ETH Zurich)
Francesco Corman, Eidgenossische Technische Hochschule Zurich (ETH Zurich)

Assigning inspection trains to monitor the tracks' quality is a standard procedure for maintaining railway systems' safety. The main challenges lie in lacking time and resources to perform the inspections due to the increasing traffic nowadays. To overcome these challenges, many consider adopting the On-Board Monitoring (OBM) technique for performing the inspections. This technique assigns commercial trains, instead of traditional Track Recording Vehicles (TRV), to monitor the tracks' status, allowing railway operators to perform more inspections without affecting the traffic and using expensive inspection trains as well. However, compared with TRV data, the new OBM data are of lower data quality and fewer features, although they can be recorded more frequently. Therefore, new methods should be developed for effectively applying the new data. This study develops four models, including the linear regression (LR) model, Markov model, Ordinary Kriging model and Kalman filter (KF) model, for predicting the tracks' status based on the OBM data. Data collected from the Switzerland railway network are used for verifying the models. Results show that the proposed models can effectively predict the degradation of tracks' status in different ways and, therefore, assist the railway regulators in scheduling maintenance tasks.

Minhao Xu (xuminhao@my.swjtu.edu.cn), Southwest Jiaotong University
Bin Shuai, Southwest Jiaotong University
Zongsheng Sun, Southwest Jiaotong University

The quality of the districting plan for the Comprehensive Inspection Train (CIT) has a direct impact on the formulation of subsequent plans, which in turn affects the operational safety of the high-speed rail (HSR). However, it relies on manual programming and adjustment for long time, which makes it difficult to ensure the reasonableness and fairness of the districting plan. For solving this problem, we construct a mixedinteger linear programming model, which aims to minimize the task volume deviation among CITs and optimize the compactness of each CIT sub-network, comprehensively considering constraints such as line coverage integrity, inspection under up-to-speed conditions, line-vehicle special attribute matching, and inspection area connectivity. The model we design linearly expresses complex matching relations with downward compatibility characteristics, and gives a connectivity determination method under non-unique matching relations by constructing a multi-commodity network flow model. Finally, we improve the traditional ideal point method to linearize the bi-objective into single-objective, and design various test cases to analyze the effects of the input conditions on the solution quality and solution speed. The results show that the key factors affecting the quality of the districting plan are the heterogeneity of CIT speed levels and the compatibility of CIT with special lines, and conclude that the network size and the number of CITs have the most significant effect on the solution speed.

Shadi Fathi, Aston University
Moura Mehravar, Aston University
Mujib Rahman, Aston University

The railway track substructure in the UK is a major component of the oldest railway systems in the world and is subjected to failure during its service life. This failure is often due to the buried drainage network malfunctioning. As a result of drainage malfunction, local soil weakness occurs which leads to the railway substructure failure. There are different non-destructive tests (NDTs) for condition assessment of railway substructure, however, the existing interpretation methods of the NDTs data provide limited knowledge on the presence, size, and location of any local soil weakness. Limited knowledge of the current condition of substructure layers leads to the employment of time-consuming and costly maintenance actions. Therefore, in this paper, a hybrid back-analysis technique based on artificial neural network (ANN) and genetic algorithm (GA) was developed to estimate the substructure layers’ moduli and identify any soil weaknesses. To this aim, firstly, a finite element (FE) model of a railway substructure layers including drainage pipe, under falling weight deflectometer (FWD) testing was modeled. In the FE model, various scenarios of the local soil weakness around a drainage pipe with different geometries and physical properties were considered. The FE model was used to generate a database for ANN training. Then, GA was employed to optimize the substructure layer moduli, soil weakness moduli, and geometrical properties of the weakness zone. The proposed technique is computationally efficient, with no dependency on seed modulus values, which can estimate substructures’ layer moduli and presence of any soil weakness in the railway substructure.

Yuning Wu, University of Utah
Xuan Zhu (xuan.peter.zhu@utah.edu), University of Utah

Rail defects, especially transverse defects (TDs), can pose risks to safe and efficient railroad operations. Effective rail defect detection is critical for the prevention of broken rail-induced accidents and derailment. In this study, a deep autoencoder (DAE) rail defect detection framework is developed to process ultrasonic A-scan data collected by a roller search unit (RSU) and identify the presence of TDs in rail samples. Autoencoder is a semi-supervised learning algorithm that identifies observations in a dataset that significantly deviates from the remaining observations and can be used for rail defect detection. The ultrasonic A-scan signals collected from pristine and damaged rail segments are analyzed, where the pristine dataset is used to train a DAE model. To improve the accuracy and sensitivity of defect detection, we optimize the architecture and hyperparameters of the DAE model. Moreover, we evaluate the performance of two features extracted from the DAE model through receiver operating characteristic (ROC) curves and confusion matrix. The DAE features outperformed conventional handcrafted features in terms of accuracy and robustness of defect detection, especially with the presence of noises. Keywords: Anomaly detection, ultrasonic A-scan, rail defect detection, autoencoder, feature learning, nondestructive evaluation

Alireza Enshaeian, University of Pittsburgh
Matthew Belding, University of Pittsburgh
Piervincenzo Rizzo, University of Pittsburgh

An inspection technique for the estimation of longitudinal stress in continuous welded rails (CWR) is presented in this article. The estimated stress is ultimately used to infer the rail neutral temperature (RNT). The technique is based on the use of finite element modeling (FEM), vibration measurements, and machine learning (ML). The FEM is used to establish the relationship between the boundary conditions and the longitudinal stress of any given CWR to the vibration characteristics (mode shapes and frequencies) of the rail. The model is used to train a ML algorithm that is then tested using field data obtained by an array of accelerometers bonded on the rail beam of interest. In this study, the proposed technique was verified experimentally during few days of test conducted in Colorado. A commercial FEM software was used to model the rail track as a short segment repeated indefinitely and under varying boundary conditions and stress. Three ML models were developed using hyperparameter search optimization techniques and k-fold cross validation to infer the stress or RNT from the frequencies of vibration extracted from the time waveforms recorded by accelerometers. The results of the experiments demonstrated that the success of the technique is highly dependent on the accuracy of the generated model. In addition, the results demonstrated that the ML was mostly able to learn from the experimental data and successfully predict the neutral temperature.

Gang Xu, Southeast University
Yunhong Yu, Southeast University, Jiulonghu
Tianling Wang, RWTH Aachen University

The application of asphalt concrete waterproofing layer (ACWL) for the subgrade has been a trend in Chinese high-speed railway. The purpose of this research is to discuss the dynamic characteristics of full cross-section ACWL in the ballastless track structure under the train loads. The 3D finite element model for the interaction system of vehicle and ballastless track structure was presented and validated by field measured data. Results displayed that the tensile strain at the bottom of the ACWL was at a relatively low level and the vertical dynamic responses of each structural layer are obviously reduced due to the application of ACWL. Meanwhile, compared with the traditional waterproof closed layer structure, the energy dissipation caused by the viscoelastic characteristics of asphalt concrete layer is beneficial to reduce the overall vibration level and vertical deformation of track and roadbed structure. Therefore, the full cross-section ACWL helps to reduce the vibration of the track structure and maintain the long-term stability of the subgrade.

Dian Setiawan (diansetiawanm@tamu.edu), Texas A&M University
Dian Setiawan, Universitas Muhammadiyah, Yogyakarta
Yong-Rak Kim, Texas A&M University, College Station
Mohammad Rahmani, Texas A&M University, College Station

Indonesia’s conventional track design for the ballast, sub-ballast, and subgrade layer is completely empirical. The current specification includes a constant thickness design for the ballast, 0.3 m, and the sub-ballast layer, 0.4 m, which is operated for the current passenger trains with 120 km/h of design speeds. Historically, conventional track has been the only option for Indonesia's railway constructions. However, the performance of the conventional track poses significant challenges for the Indonesian government's desire to enhance train speed. Therefore, an improved track design is essential to increase train's operation speeds and minimize possible damage generally observed on the conventional tracks. A more mechanistic approach is recommended since it can fundamentally ensure the optimum track materials and their structural configuration. Toward that end, this paper presents a computational modeling and simulation of asphaltic rail tracks by varying the type (i.e., asphaltic underlayment and asphaltic overlayment) using a two-dimensional finite element method. Model simulation results of asphalt layer deformation and vertical stress of subgrade layer were compared from a parametric analysis with varying track configuration, layer material properties, and train speed. The asphaltic overlayment track was better performed in terms of vertical stress attenuation in the subgrade, whereas the asphaltic underlayment track performed better in terms of deformation of the asphaltic layer. This study sheds light on the potential application of asphaltic rail track to the Indonesian railway system and contribute to the literature on the use of mechanistic approach for an optimal rail track structural design and selection of track materials.

Luv Sehgal, Arup
Amit Garg, Indian Railways Institute of Civil Engineering

With the assumption that the conventional railway track structure with a concrete tie and rail web resting on 12 inches of ballast has reached a level which has matured beyond the possibility of any classical enrichment from research, a new look at the basic track structure has been a no-go area. However, with unprecedented growth in speed and wheel loadings, the rail industry must find new technologies to provide a stronger, safer and low maintenance structural solutions.  Asphalt, with its multi-faceted and adjustable behavior is perfectly suited to contribute towards the long-standing problem of uneven stiffness of the granular sub ballast layer that a ballasted rail track is afflicted with. A lot of research and trials have been done in the European and Asian railways and a few instances of trial applications are available in the American Railways as well.  Bituminous sub ballast with optimum resilience has now proven to be an alternative to the conventional granular sub ballast. The results of several studies reveal that the structural performance has improved when a 6-8 inch conventional bituminous subballast layer was used in lieu of granular layers.  This paper consolidates the knowledge available on the subject through an extensive literature review and models different trackbed structures on Kentrack 4.0 that uses Superpave-mix design to establish that provision of an asphalt subbase layer below the ballast is not just a structural necessity for high-speed railways but has the potential to replace the granular sub-ballast in conventional railway track structure.

Yihao Ren, North Dakota State University
Chengbo Ai, University of Massachusetts, Amherst
Pan Lu, North Dakota State University

In the United States, around 1/3 of the rail network is operated by shortlines (Class II and III railroads). These railroads play an important role in the nation’s transportation system since they serve as the feeder and distributor for the rail network and handles the first and last mile of freight shipments. However, due to their limited financial resource, these railroads are usually lack of a comprehensive rail track inventory for timely and efficient rail asset managements. Much research has been conducted to develop automatic rail extraction methods, since it is a critical step toward a comprehensive rail infrastructure inventory. However, existing methods strongly rely on high-density point cloud dataset, sensor property and configuration, assumptions on global features such as rail orientation and track bed height; therefore, their applications in shortlines are greatly limited, since rail tracks will travel through different terrain with various global features, and datasets owned by shortlines are mostly low-density datasets with unknown sensor property and configuration. To address these limitations, this study proposes an automatic and configuration-independent rail extraction method that only rely on local features. The proposed method is tested on the existing grade-crossing dataset collected by Federal Railroad Administration with a point density of 293 points/m 2 around the track bed area. The performance of the method shows an average completeness of 97.1%, correctness of 99.7%, and quality of 96.8%. The proposed method provides shortlines the capability of establishing their own rail track inventory and a great potential for future geometry measurements.

Hussam Khresat, Southern Methodist University
Jase Sitton, Southern Methodist University
Brett Story, Southern Methodist University

Low-clearance rail bridges over roadways are susceptible to vehicle strikes by overheight vehicles and equipment exceeding posted clearances. The Federal Railroad Administration (FRA) mandates post-strike inspections and evaluations of bridges after such a strike. A real-time strike detection system streamlines rail monitoring efforts by providing strike detection and characterization. Artificial neural network (ANN) systems distinguish strikes from normal operations, i.e. passing trains, by evaluating data streams from in-situ monitoring systems. While ANNs represent a method of enhancing structural evaluation efforts, the performance of such a system is dependent on input data streams, network architecture, and the amount of available training data. All three components are interdependent and must be carefully designed to optimize specific system performance. This study assesses performance of several ANN architectures on combinations of real-world data streams from four instrumented rail bridge structures. Specifically, the effects on performance of the network size, data stream character, and data stream augmentation are investigated. Results indicate improved performance metrics for several convolutional neural networks (CNNs) by using multiple channels of acceleration signals. Bridge strike detection is increased further by increasing the amount of data used to train the network. False positives may be decreased by increasing network complexity.

Mingu Kang, University of St. Thomas
Han Wang, University of Illinois, Urbana-Champaign
Issam Qamhia, University of Illinois, Urbana-Champaign
Erol Tutumluer, University of Illinois, Urbana-Champaign
Younes Haddani, Sol Solution
Adam Bankston, BNSF Railway

The progressive degradation of railway ballast over time increases the degree of ballast fouling and poses significant drainability concerns leading to frequent maintenance activities. To avoid such issues, an effective and continuous characterization of the physical properties of the ballast layer is deemed necessary. This paper introduces the combined use of a bender element (BE) piezoelectric field sensor and a PANDA dynamic cone penetrometer for periodic ballast condition evaluations over the traffic use. To simulate various levels of ballast degradation, box tests were conducted using a dolomitic ballast material compacted in a laboratory-sized testbed with fouling indices ranging from 0 to 39%. BE sensor pairs were embedded in the ballast layer to calculate the small-strain modulus behavior of the ballast assessed through shear wave velocity measurements under different confinement conditions. Strength profiles of the ballast at different fouling levels were also measured using the PANDA penetrometer. Experimental findings from both test methods indicate the existence of a dense state of ballast linked to a certain fouling index that maximized stiffness and strength characteristics of the tested ballast in dry condition. In accordance, the potential use of BE field sensors embedded within in-service ballasted track coupled with periodic penetration testing can be a viable approach to evaluate ballast layer degradation. Various void packing conditions associated with ballast fouling levels can be linked to important ballast layer modulus and strength behavior. Further, such smart sensing of ballasted track behavior can provide long-term performance monitoring solutions for ballast cleaning and maintenance scheduling.

Yufeng Gong, University of South Carolina
Yu Qian, University of South Carolina

Evaluating railway ballast fouling condition is critical to assessing track conditions and arranging proper ballast maintenance. Because fouled ballast materials with different fouling conditions have different material properties, these properties could be used to evaluate the fouling severity. Although approaches using Ground Penetration Radar, Impulse Response, Surface Wave, and SmartRock have been developed to estimate the fouling conditions, these methods require special sensors or equipment, and well-trained technicians. Recently, convolutional neural network (CNN) based computer vision approaches have become popular in performing particle segmentation to obtain the ballast grain size distribution. Unfortunately, most of those approaches could not segment fine particles, and only the coarse aggregate fraction can be evaluated. This study proposes a novel image analysis approach to directly estimate the ballast fouling conditions. First, fouled ballast images with different fouling conditions are taken as the reference. Then, the RGB color distributions of the fouled ballast images are processed through statistical analysis. A strong linear correlation between Fouling Index (FI) and Variance is found and used to establish an FI prediction model. The established FI prediction model is tested and validated by additional fouled ballast samples. The proposed FI prediction model in this study can be used to quantify ballast fouling conditions with promising performance.

Shihao Huang, University of South Carolina
Yu Qian, University of South Carolina

Crushed aggregates with irregular shapes and sharp corners are favorite ballast materials to ensure proper railroad track bearing capacity of owing to good contacts between the particles. Meanwhile, crushed aggregates are prone to deterioration and breakage under repeated train loads. The effect of particle degradation on ballast mechanical behavior has been investigated through large-scale triaxial tests and discrete element method (DEM) simulations before. Unfortunately, previous research rarely separated ballast breakage during compaction from breakage during shearing. The ballast breakage during compaction and its influence on ballast mechanical behavior are not thoroughly understood. This study investigates the effect of ballast particle breakage during compaction through large-scale triaxial tests and DEM simulations. Clean and new ballast particles are painted using different colors to trace potential breakages after each triaxial test. Samples compacted with and without a rubber pad are prepared for comparison. DEM simulations are performed using a newly proposed particle breakage model capturing ballast corner breakages. The results show that corner breakage is the major particle breakage mode and mostly happened during the compaction instead of during the shearing process. The results also indicate ballast corner breakage during sample compaction would increase the peak strength of the ballast but would not change the Young’s modulus. A rubber pad is strongly recommended to reduce ballast breakage during the triaxial test sample compaction.

Shushu Liu (shushu.liu@dot.gov), OST-R/Volpe Center
Theodore Sussmann, OST-R/Volpe Center
Kyle Riding, University of Florida
Cameron Stuart, Federal Railroad Administration (FRA)

Under tie pads (UTP) and under ballast mats (UBM) have been increasingly used in rail track construction to reduce track stiffness and increase ballast contact area with track support elements either above the ballast (ties) or below the ballast (e.g., subgrade, tunnel invert, etc.). Locations such as tunnels, bridges, and bridge approaches are strong candidates for UTP and UBM use due to the high support stiffness they provide to the ballast and the corresponding high stress level. A research program was conducted in the Washington D.C. Virginia Ave tunnel to monitor track performance with UTP and UBM over time along with variations in track support parameters including tunnel invert pressure, tie bottom pressure, and track deflection, etc. A finite element track model was developed based on the tunnel track monitoring data. The model was extended to estimate track behavior without UTP or UBM. The results highlight the individual contributions of both the UTP and UBM to the stress reduction on the ties and tunnel floor. The model was modified to simulate the behavior of clean granular materials like ballast that tend to develop load chains that can lead to high peak stress, cause localized damage and increase the rate of ballast and structure degradation. UTP and UBM were found to reduce the average stress on tunnel floor by 5-20% when the ballast was well compacted while peak stress resulting from ballast load chains can be significantly reduced by 50% or more.

EBERECHI ICHI, University of North Dakota
Sattar orafshan, University of North Dakota

Inspection and monitoring of rail sub-structural elements such as ballast is important in railway management system to ensure safety, serviceability, stability, and durability. Ballasts are continuously exposed to cyclic loads and harsh environmental conditions which obstructs their effective water-draining function. Conventional evaluation of water content in ballast requires contact which is laborious, costly, time inefficient, objective, prone to error in judgement, unsafe, disruptive to traffic flow, or destructive to railway structure, stability, durability, and serviceability. In this study, the authors have investigated the feasibility of using diffused reflectance spectroscopy to detect water and fouling contamination in railroad ballast. In has also been shown that that presence of water or fouling material impacted light incident reflectance which were measured using noncontact hyper-spectral imagery at visible near infrared (VNIR) and near infrared (NIR) bands. A 15kg ballast material sample was prepared according to the American Railway Engineering and Maintenance-of-Way Association (AREMA) gradation-5 requirements. The sample’s moisture content was varied from 0g to 700g for wet condition, submerged condition. Additionally, partially submerged ballast with different degrees of fouling were investigated. The reflectance profiles of different wet conditions were seen to be distinct from each condition and as well from the dry condition. The reflectance and absorbance decreased and increased respectively between 1375 to 1550 nm for the NIR and 450 – 500nm for the VNIR. Therefore, a negative correlation for the water content-reflectance and positive linear correlation for water content-absorbance relationship.