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2025 | Book

Proceedings of the 5th International Conference on Transportation Geotechnics (ICTG) 2024, Volume 1

Sensor Technologies, Data Analytics and Climatic Effects

Editors: Cholachat Rujikiatkamjorn, Jianfeng Xue, Buddhima Indraratna

Publisher: Springer Nature Singapore

Book Series : Lecture Notes in Civil Engineering

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About this book

This book presents select proceedings of the 5th International Conference on Transportation Geotechnics (ICTG 2024). It includes papers on ground improvement methodologies, dynamics of transportation infrastructure, and geotechnical intricacies of mega projects. It covers topics such as underground transportation systems and heights of airfields and pavements. This book discusses diverse thematic landscapes, offering profound explorations into sensor technologies, data analytics, and machine learning applications. The publication highlights advanced practices, latest developments, and efforts to foster collaboration, innovation, and sustainable solutions for transportation infrastructure worldwide. The book can be a valuable reference for researchers and professionals interested in transportation geotechnics.

Table of Contents

Frontmatter
Use of Unmanned Aerial Vehicles (UAVs) for Transport Pavement Inspection

Technological evolution has allowed the use of unmanned aerial vehicles (UAVs) in an easier and more diversified way, creating opportunities for its application in various fields of engineering, namely in the inspection of transport infrastructures. The present study begins with the analysis of the main practices that resort to the use of UAVs, in order to frame its application in the field of transport pavement inspection. A review of studies and other available literature served as a starting point to define the methodology adopted for the development of the case study presented. The methodology includes the collection of images of a flexible road pavement section, its processing, and the creation of an orthoimage and a 3D model from which it was possible to identify and characterize the distresses present on the pavement surface. The main results obtained point to planimetric and altimetric deviations of less than 2 and 10 mm, respectively, for the images collected by the Mavic 2 Pro drone at 3 and 20 m high. With the collected data, it was also possible to calculate the global quality index PCI for the inspected pavement section. Under these conditions, it is possible to conclude that the accuracy is very good and suitable for the intended purpose, allowing fast data collection at low cost. This new technological approach supports infrastructure managers in the design of maintenance programs and in the scheduling of interventions, thus contributing to the increase of the durability and safety levels of the inspected pavements.

Bertha Santos, Pedro Gavinhos, Pedro G. Almeida, Dayane Nery
Integrity Testing of Diaphragm Walls by Thermal Methods

Distributed measurements of temperature in drilled or bored pile foundations to assess the integrity of the curing concrete have become increasingly common in the past decade. These methods have the advantage of detecting significant anomalies inside and outside of the reinforcing cage as the initial hydrating of the cement in the concrete generates heat. Conditional acceptance or further review of the foundation can be obtained within one to two days of casting. More recently, these techniques have been applied to diaphragm wall panels used in tunnels and other building construction. The expected trends in the temperature versus depth data are reviewed, indicating the consistency of measurements on the wall faces and cooler zones at the corner. An example showing the detection of local concrete cover changes and potential inclusions or non-uniformities at the bottom of the panels are also presented.

Brent R. Robinson, Matthew Baudo, Richard C. L. Yu
An Application of Geotechnical Instrumentation Permanently Embedded in Railway Track

Researchers and engineers rely on information and data to study, operate, maintain, and improve transportation infrastructure. Measurement provides quantitative information and evidence for these purposes. Sensors for measurement can be embedded into the track system at a fixed location or vehicle mounted. This paper describes the design, deployment, and operation of a permanent measurement system that uses sensors embedded in the trackbed on an operational railway in the United Kingdom to monitor track deflection, stiffness, and pressures at key trackbed interfaces. The measurement systems comprise a longitudinal array of accelerometers fixed to sleeper ends; dynamically sampled total pressure cells placed at the ballast-subgrade interface; and total pressure cells, piezometers, and accelerometers buried in the subgrade. Permanent, fixed sensors provide large volumes of detailed but highly localized information, while multichannel sensor arrays produce data that describes performance along the track. Methods for data reduction are proposed for the acceleration measurement system that allows spatial and seasonal variation to be visualized along the instrumented site for different train types. This approach enables comparison with track geometry data from rolling (on-train) measurements. Locally, buried instrumentation provides evidence for variation in load transfer to the subgrade both along the line and across individual sleepers; illustrates how interaction between wheelsets varies with depth and by vehicle type; and how the pore water responds to train loading. The application of measurement for monitoring purposes permits an analytical approach to data interpretation where monitoring parameters are similar to those used for design and simulation.

David Milne, Geoff Watson, William Powrie, Ben Lee
Transforming Construction Waste into Eco-Friendly Vegetated Embankment Slopes: A Path to Sustainable Infrastructure

Construction and demolition wastes (CDW) are being explored as a viable alternative material for geo-environmental applications, including embankment fill material for roads and railways. However, there are still limitations to CDW use, specifically on its environmental effect. Maintaining a well-balanced relationship between construction and the environment is crucial to avoid ecological problems. Hydrochar obtained through hydrothermal carbonization can adapt CDW for the growth of plants needed to stabilize CDW embankment slopes. Given the highly alkaline nature of CDW and its potential environmental impact, hydrochar presents a possible solution to improve the efficiency of CDW as an embankment material and plant medium. However, there is a lack of research to prove the feasibility of using hydrochar to fully utilize CDW in transportation engineering applications through greening. In this study, hydrochar was produced from peanut shell and wood biomass at 250 °C and 1 h of residence time. To assess the chemical and physical properties of hydrochar for CDW remediation, pH and electrical conductivity tests, proximate analysis, surface area and pore volume tests, and Fourier Transform Infrared Spectroscopy were conducted. Compaction tests were also conducted on the hydrochar-amended CDW. The acidic nature and porous structure of hydrochar have significant implications for positive improvement of CDW properties.

Trishia Liezl Dela Cruz, Ekaterina Kravchenko, Charles Wang Wai Ng
A Comparison Between Traditional and Satellite Monitoring by Means of Dinsar Technique Within the Framework of the Construction of Metro Line 1 in Naples

The Naples Metro Line 1 is an important infrastructural work that extends for approximately 18 km, connecting 19 stations in the city. The project is developed in a particularly complex geological context, characterized mainly by soils and rocks of pyroclastic origin and marine sands. This article focuses on the monitoring of subsidence movements around the stations of Università and Toledo stations, as it is of particular geotechnical interest due to the dense urban and construction context surrounding it. Traditional monitoring operations carried out through optical leveling between 2002 and 2009, encompass both the underground stations and connecting tunnels. The aim of this study is to compare the data obtained through the remote sensing technique DInSAR (Differential Synthetic Aperture Radar Interferometry) using measurements from the ENVISAT (2002–2010) sensor, with the results of traditional monitoring. The data from the COSMO-SkyMed constellation, of the Italian Space Agency, are then analyzed to evaluate any posthumous movements that have occurred along the same section since 2010, therefore in the post-construction phases, in order to identify any critical issues that may require further investigation.

Manuel De Luca, Gianpiero Russo, Marco Valerio Nicotera, Diego Di Martire, Ilaria Esposito
Deep Learning Approach for Automated Railroad Ballast Condition Evaluation

Ballast has a significant impact on track performance, and the evaluation of ballast condition is crucial for safe railroad operations. This paper focuses on a Ballast Scanning Vehicle (BSV) recently developed for automating ballast inspection using a deep learning-based, computer vision approach. Traditional evaluation methods, e.g., visual inspection or ballast sampling followed by sieve analysis, are subjective and labor-intensive. Furthermore, ballast samples collected from a single location/depth may not be representative of accurately revealing variations of degradation and ballast condition along the track. In contrast, the BSV employs three image acquisition devices to continuously capture high-quality scans of ballast cut sections, enabling accurate and in-depth evaluation of continuous sections of the track. The deep-learning framework was trained to process acquired ballast scans, generating image-based metrics including percent degraded segments (PDS), fouling index (FI) estimates, and in-service ballast gradations. The accompanying user-friendly graphical interface integrates all data processing algorithms and provides comprehensive visualizations of results. Field data was collected using the BSV, from cut trenches opened using a ballast regulator, along the High Tonnage Loop (HTL) at the Transportation Technology Center (TTC) in the U.S. The FI and gradations from the BSV were compared to laboratory sieve analyses and Ground Penetrating Radar (GPR) data. Additional laboratory tests with various fouling conditions were conducted to validate the deep learning algorithms and clarify any differences between sieving results and algorithm estimates possibly attributed to sampling issues. This field deployment demonstrated that the BSV could accurately evaluate ballast conditions close to real time, thus making it a robust system for quantifying ballast degradation.

Jiayi Luo, Kelin Ding, Issam I. A. Qamhia, John M. Hart, Erol Tutumluer
Smart Pebbles to Monitor Aggregate Response Under Repeated Loading

This study investigates aggregate particle movements under repeated loading in a controlled laboratory environment. Smart Pebbles, 3-D printed aggregate-shaped particles instrumented with gyroscopes and accelerometers, were used to capture particle movements in a geogrid-stabilized triaxial specimen subjected to repeated load pulsing. The study aims to understand the impact of geogrid stabilization on aggregate particle movements within an unbound base layer subjected to moving wheel loads, a critical determinant of layer stability. Two Smart Pebble sensors were placed at different depths in triaxial specimens. Each test configuration was designed to monitor and comparatively assess the aggregate movement in relation to its spatial position in the specimen. The results reveal a reduction in translational and rotational acceleration of the Smart Pebble where a geogrid is present, while the sensor placed near the top of the specimen experienced a higher translational acceleration. The Smart Pebble could thus be used as a tool to quantify the zone of influence of a geogrid and could be utilized for structural health monitoring of unbound granular layers in pavements.

Syed Faizan Husain, Mohammad Shoaib Abbas, Han Wang, Issam I. A. Qamhia, Erol Tutumluer, John Wallace, Matthew Hammond
Use of Bamboo Piles in Ground Improvement Design—Case Study

A semi-rigid ground improvement method using bamboo poles is used in some countries in the Southeast Asian region, to improve land underlain by weak clays. It is a technology often used in projects in some areas of Indonesia where soft soils are common. Although this approach is adopted as a common ground improvement technique, the design is largely based on local experience. The semi-rigid inclusion consists of several individual bamboo poles bundled together as a cluster. In some projects a prefabricated vertical (wick) drain is also attached to the bamboo cluster to facilitate a shorter drainage path length. Following the installation of bamboo clusters, a bamboo raft is placed over them as a load transfer platform. This ground improvement technique is known as Bamboo Pile Raft System (BPRS). Although its performance under vertical loading has been found satisfactory in several projects, its performance under combined loading (vertical and horizontal) has not been understood well. The performance of a breakwater under combined loading was reviewed through numerical analysis to understand the behaviour of the BPRS ground improvement method. The results indicate that transient loading during construction and the depth of improvement with bamboo clusters in relation to weak clay thickness is critical. The results also show the increased strength gain due to pore pressure dissipation through wick drains incorporated in the bamboo clusters which helps improve the BPRS performance.

Thayalan Nall, Jay Ameratunga, Andreas Putra
The Carbon Footprint of Vibratory and Impact Rolling: A Sustainable Option for Bulk Earthworks on Infrastructure Projects

Vibratory and impact rollers achieve deeper lift compaction than static rollers. Ground improvement with impact rollers occurs through rolling dynamic compaction, enabling compaction to significant depths, generally more than 1 m. This provides the opportunity to place thick layers, potentially with a larger maximum particle size than conventional smooth drum rollers, while achieving engineering standards of density and stiffness. The overall consequence of this is that the earthworks exercise becomes a far more sustainable activity. Deeper lift compaction beyond traditional thin compacted layers using conventional heavy vibratory rollers has been achievable for some time, but to lesser depths than is possible with impact rollers. The compaction of deeper lifts at faster operating speeds, albeit, typically with a greater number of passes, requires a fresh look at specifications for infrastructure earthworks. The paper explores the green credentials of deep lift compaction, by comparing earthworks plant, productivity and fuel usage for compaction using conventional circular drum rollers with thin layers, and deeper lift compaction using vibratory and polygonal impact rollers. Quality control to greater depths can be a limiting factor. Testing protocols often require modification to accommodate the changes in layer thicknesses and material specifications.

Derek Avalle, Burt Look, Brendan Scott
Design and Construction Monitoring of a Road Embankment and an Underpass Founded on Soils Susceptible to High Static Settlement and Liquefaction

The Waikato Expressway is one of the New Zealand Transport Agency’s (NZTA), now Waka Kotahi, seven Roads of National Significance. Tetra Tech Coffey is part of the City Edge Alliance (CEA) engaged by Waka Kotahi to design and construct the Hamilton Section of the Waikato Expressway project. As part of the project, the CEA was required to design and construct an 8 m high embankment founded on soil consisting of highly compressible soft Peat, Clay, and liquefiable embedded Sand layers. Preloading the subsoil with a high surcharge was recommended to minimise the post-construction settlement at the expressway embankment and underpass concrete box structure at Powells Road. Contiguous flight Auger (CFA) lattice was utilised post-surcharge to improve the ground further at the underpass location. The CFA ground improvement was not utilised in the earthwork’s embankment portion on either side of the underpass to minimise the construction costs. Planning and designing a new 8 m high soil embankment close to a CFA soil improvement zone increased the need for accuracy in differential settlement and complexity of the work. Limited laboratory testing and underestimated compressible soil parameters caused delay in the settlement rate in the initial stage and additional surcharge was placed to speed up the consolidation period. Several settlement monitoring instruments were installed and several settlements back analysis methods using finite element modelling and empirical methods were used to observe the settlement rate and estimate the additional surcharge requirement. This paper presents the design and monitoring of an expressway embankment for static and dynamic loads, action taken to stabilise the surcharge slope failure and other difficulties faced during construction.

Raathiv Shanmuganathan, David Sullivan
A Discussion on the Use of Geophysical Methods When Assessing Ground Conditions for Remediation Design of Road Embankment Failures

A common problem faced following a road embankment failure is undertaking site investigation, for remediation design, safely. Failure sites are often unstable and traditional methods of investigation (e.g., boreholes) pose a significant safety hazard to those undertaking the investigation and could induce further instabilities at the site. In addition to the hazards of the failure, road corridors often have to remain open to continue to provide vital transport connections for the continuation of freight movement, people movement, and emergency services (e.g., ambulance, fire and rescue) which adds further constraints when choosing site investigation methods. Geophysical techniques provide a potentially safer alternative to traditional boreholes when investigating failure sites with the ability to be flexible with the continuation of road traffic. This paper presents a discussion of the advantages and limitations of some of the available geophysical techniques that could be utilized when conducting geotechnical site investigations at road embankment failures. A recent case study of a road embankment failure is also presented where the use of geophysical methods was utilized due to borehole drilling being deemed unsafe at the failure site.

Reagan Newton
A Case Study on the Bearing Capacity of Large Diameter Bored Piles Plugged in Weathered Limestone for Cable-Stayed Bridge

For bridge projects, load testing for large-diameter bored piles in the middle of a river is a challenge for traditional static load testing methods. The Bi-directional Static Axial Compressive Load—BSACL (ASTM D8169-18) solution is a reasonable method with the advantages of not using weight load and faster progress. This article presents the results of BSACL for a 1500 mm diameter bored pile, 45.5 m long, with the pile tip embedded in a weathered limestone layer. This is a case study of the Hieu River cable-stayed bridge in the center of Vietnam with a 200 m length span. The maximum load reached 17,250 kN applied through a Load Box placed at a depth of −37.4 m, with a movement of the Load Box approximately 11.9 mm upward, 9.8 mm downward, and 6.3 mm at the pile head. The side resistance in the slightly weathered limestone layer (RQD = 30%) achieved 350 kPa, and in the highly weathered limestone layer reached 175 kPa (RQD = 0%). The side resistance of the soil layers above the Load Box is also analyzed through strain gages simultaneously. The research results could help engineers to understand the load distribution along the bored piles.

Do Huu Dao, Pham Van Ngoc, Huynh Phuong Nam, Ho Dac Khanh Minh
Preloading as a Sustainable Ground Improvement Solution for Road Infrastructure

Construction of infrastructure over soft soils presents significant challenges for sustainable foundation solutions due to low bearing capacity, high compressibility, ongoing long-term creep and onerous design performance criteria. The commonly adopted solution to these challenges is to construct a rigid structure which is often carbon intensive, costly and does not necessarily circumvent all differential settlement issues. This paper presents a case study of the preloading treatment design of a road embankment at a site in Wentworth Point, NSW, underlain by soft reclaimed and alluvial sediments between 12–20 m deep. Ground improvement through preloading and surcharging was proposed for the new road infrastructure servicing the development buildings, in lieu of piled foundations or rigid inclusions adopted for neighbouring developments. By using clusters of investigation (boreholes, CPT, sDMT) with laboratory testing, detailed ground profile interpretation was possible to develop Finite Element models to predict soft soil creep model under proposed treatments. During the ground treatment period, the contractor and design team adopted an observational method in determining the treatment period, following a set monitoring regime and a response plan. This case study includes a discussion on the considerations and lessons learned in pursuing a more sustainable foundation solution in soft soil including monitored impacts of Prefabricated Vertical Drain Installation and the value of plotting data differently to see what is happening through a different lens.

Alvin Chen, Evan Kailis, Sergei Terzaghi
Improving Low-Lying Acidic Floodplains for Infrastructure Development

In coastal Australia, shallow pyrite deposits (FeS2) in low-lying terrains oxidise to produce sulfuric acid that pollutes the soil and groundwater, thus adversely affecting the environment, coastal development, fisheries, and agricultural development. Permeable Reactive Barrier (PRB), an underground granular filter, is a practical engineering technique that neutralises groundwater acidity. Two pilot-scale PRBs were installed in the lower Shoalhaven floodplain, NSW, Australia, to treat the acidic water using alkaline aggregates as the reactive material. However, during the treatment of contaminated groundwater, the entrapment of biomass generated by bacteria and secondary mineral precipitates within the granular matrix reduces the hydraulic conductivity and porosity of the PRB and affects its longevity. Real-time monitoring data of the field PRB and its clogging patterns along the centreline of the PRB are discussed in this paper. Clogging and armouring were non-homogeneous along the flow path, and the acid neutralisation capacity at the inlet of the PRB decreased by 31% in 15 years due to clogging, but only a 6% reduction was observed at the outlet.

Subhani Medawela, Buddhima Indraratna
Semi-analytical Wavefield Modelling for Pavement

This study presents an efficient semi-analytical wavefield modeling approach to generate surface wave dispersion spectrum for pavement-type structures. The method addresses the challenges posed by complex wave propagation through inversely dispersive medium, where the observed velocity spectrum often presents numerous mode branches. It computes theoretical dispersion curves, including real and leaky modes with complex wavenumbers by employing an eigenvalue-based higher-order thin layer method. Subsequently, the frequency domain dynamic surface responses are obtained to simulate in-situ pavement testing. The proposed approach is implemented on two pavement profiles: one representing an asphalt pavement model and the other a rigid concrete pavement model. The dispersion spectrum for the asphalt pavement has been validated against published numerical simulation. The study highlights that the fundamental antisymmetric mode of Lamb wave generated by the top layer dominates the wavefield. The overall dispersion trend can be utilized to estimate the properties of the top stiff layer. The low-frequency dispersion spectrum shows multiple modes corresponding to the base embedded layers underneath the surface layer. With the proposed wavefield modelling approach, the entire dispersion spectrum can be inverted to obtain the properties of the base course layers, eliminating the challenges to select specific modes.

Mrinal Bhaumik, Tarun Naskar
Predicting the Shear Strength of Granular Waste Materials Using Machine Learning

Knowing the shear strength of soil is imperative for geotechnical design as shear failure, combined with excessive deformations, is the predominant failure mechanism within a loading environment. However, determining the shear strength in the laboratory is often laborious and hence costly. Moreover, a granular material’s behavior is complex which can compromise the accuracy and robustness of predictive models developed through traditional methods. This is exacerbated when considering non-traditional waste materials such as steel furnace slag, coal wash, and scrap rubber due to their increased nonlinearity and variability. Consequently, previous relationships and models proposed are often self-contained and break down when extrapolated beyond specific loading conditions or material types. In this study, predictive models for the peak friction angle (ϕ′peak) of various granular mixtures (waste and non-waste) were developed using two nonlinear machine learning (ML) techniques, namely, artificial neural network (ANN) and second-order multivariable regression (MR). Five key parameters were chosen to represent the mixture type (rubber content, median particle size), its physical properties (initial void ratio, dry unit weight), and the loading condition (effective confining pressure) using 154 consolidated drained triaxial test data samples. Although MR performed satisfactorily on both the original and secondary datasets, ANN combined with Bayesian regularisation was superior with R2 of 0.96 and 0.82 for both phases, respectively. Hence, ANN is an attractive modelling technique as it is capable of capturing nonlinear relationships for various granular mixtures (i.e., waste and non-waste, with and without rubber) to predict shear strength without the need for laboratory testing.

Haydn Hunt, Buddhima Indraratna, Yujie Qi
Bayesian Back Analysis for Settlement Prediction of Embankments Built on Soft Soils Incorporating Monitoring Data—A Case Study

A novel method for back analysis was used for an embankment over deep soft soil along a major highway upgrade between Woolgoolga and Ballina, NSW. Bayesian back analysis was undertaken using monitored settlement data. The key parameters of interest were the compression ratio, recompression ratio, creep strain rate and coefficient of vertical and horizontal consolidation. Posterior distributions were sampled using a multi-chain Monte Carlo algorithm through a likelihood function to estimate the updated model parameters and subsequent settlement prediction. The simplified geotechnical model, incorporating parameter ratios, can be shown to reduce the amount of computational time required. The predictions were shown to converge to the field measurements regardless of some assumptions made about measurement error and aided in providing a more consistent prediction based on the available data. The intent of the study was to demonstrate that key geotechnical parameters can be updated, and settlement predictions revised and verified from limited site investigation data using Bayesian back analysis incorporating monitored surface settlement data.

Merrick Jones, Shan Huang, Jinsong Huang
Machine Learning Methods to Predict Resilient Moduli Behavior of Subgrade Soils

Due to limited budgets and complex testing procedures to determine resilient modulus (MR), engineers often rely on correlations between modulus and other engineering properties such as unconfined compressive strength (UCS). However, a majority of such correlations are based on linear regression, which can often lead to under or over-prediction of the correlated values. With the advancement in computing techniques, it has become convenient to understand and predict the behavior of engineering materials using optimization techniques. In this study, two machine learning (ML) techniques, including Artificial Neural Network (ANN) and Random Forest (RF), were used to predict the MR values from UCS data. Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and coefficient of determination (R) were computed to assess the effectiveness of the models. The statistical analysis of the testing sets indicated that the RF model achieved a goodness of fit value of 0.97, an MAE of 12.7 MPa, and an RMSE of 18.5 MPa. Alternatively, the ANN model indicated the corresponding goodness of fit value of 0.71 with an MAE of 44.3 MPa and an RMSE of 60.9 MPa. Furthermore, among various contributing factors, UCS was identified as the primary factor in predicting MR values for both models. Based on these findings, the RF model outperformed the ANN model in predicting unknown data within the examined parameter ranges and provides fitting parameters depending on the nature of the datasets, which avoids the overfitting effect. Therefore, this study demonstrates a progressive understanding of the potential use of the advanced computing tool to obtain more accurate resilient modulus values from the strength data.

Sopharith Chou, Nripojyoti Biswas, Anand J. Puppala
From Non-destructive Testing to Ground Property Inference: Integration of AI and Unsaturated Soil Dynamics

The present paper introduces an innovative Artificial Intelligence (AI) framework specifically designed for the statistical inference of the characteristics of unsaturated soils. This model utilizes non-destructive indentation testing methodologies, such as Light Weight Deflectometer (LWD), providing a more advanced and accurate evaluation of soil properties. Existing methodologies for inferring ground properties present a variety of limitations that challenge their accuracy and applicability. These include an inconsistent representation of the interrelated impact of water content and dry density in the indentation results; a significant loss of precision due to their empirical nature; issues with generalization; and an inability to effectively communicate the inherent uncertainty in the ground assessment and conditions. To address these challenges, our AI-based model integrates a more holistic and comprehensive approach, improving upon these limitations and refining the process of ground inference. The novel framework enhances the overall accuracy of soil property determination and introduces a more dependable method of conveying the uncertainties within the assessments. The model's potential for effective use extends beyond theory, as it is capable of practical implementation in field trials. Our paper will showcase examples of this technology in action, demonstrating how it successfully infers soil properties in actual field scenarios.

Javad Ghorbani, Jayantha Kodikara
The Evaluation of a Commercial Back-Analysis Package in Condition Assessment of Railways

Falling weight deflectometer (FWD) test has been used widely for pavement condition assessment, and different back-analysis techniques and commercial software have been developed to predict the layers’ moduli. Despite the recent use of FWD tests for railway substructure condition assessment, there have been no reported instances of commercial back-analysis software specifically designed for this purpose and only a limited number of research-based techniques exist. Among the available back-analysis packages, BAKFAA stands out as a notable option, which was originally developed for pavement applications and asserts its adaptability for railway applications. This paper aims to investigate the suitability and accuracy of utilizing BAKFAA for railway applications, assess its performance, and determine whether it can effectively meet the requirements of railway analysis. In this study, the primary developed and validated finite element (FE) model of a railway track section by the authors is employed to generate extensive virtual experimental FWD testing data. In this regard, various layers’ moduli for each layer are considered over a predefined range to represent the real field conditions. Then the back-analysed values through BAKFAA are compared against the target values (Cone Penetration Test (CPT) data) to check the accuracy of the BAKFAA predictions. The findings of the study indicate that BAKFAA tends to overestimate the moduli of substructure layers. To address this issue and align BAKFAA with the virtual experimental data, the study proposes a correlation model. This model can enhance the applicability of BAKFAA for railway applications by making necessary adjustments based on the virtual experimental data.

Shadi Fathi, Moura Mehravar, Mujib Rahman
Application of Data Fusion to Determine the Geotechnical Model of the Substructure

In the context of railways, it is essential to have a reliable ground model to ensure their safety and smooth operation. To achieve this, geophysical monitoring, and geotechnical techniques, are often used. However, effectively combining geophysical and geotechnical data remains a challenge. In this study, a methodology based on data fusion of Ground Penetrating Radar (GPR) and Pandoscope data and based on belief functions is proposed in order to define the ground model of the railway substructure. The fusion of these data using Smets, Dempster-Shafer, and PCR6 normalization rules allows to improve the characterization of horizontal interfaces and to achieve an accurate assessment of the subsurface conditions on railway tracks.

Jorge Rojas Vivanco, Pierre Breul, Aurélie Talon, Miguel Benz-Navarrete, Sébastien Barbier, Fabien Ranvier
Retrofitting of Existing Railway Tracks Using Micropiles as a Ground Improvement Technique: Finite-Element and Genetic Programming Approach

Indian Railways has the fourth largest railway network in the world. Today, there is an urgent need for enhancing the capacity of tracks for which replacement of old tracks with new ones in a time bound manner for the vast and busy network is a challenging task. To overcome this challenge, micropiles are proposed as an alternative tool to reinforce the existing railway subgrade without removing the existing track and without blocking existing traffic. In this study, suitability of micropiles as a ground improvement method for the existing railway track was assessed. Micropiles were modelled along the sides of the track in various orientations. A complete parametric study varying the geometrical parameters of the micropile under static and realistic moving train load conditions was performed using Finite Element Method (FEM) software. Three-dimensional numerical models with variations in length, diameter, inclination and spacing of micropiles was also performed. The results of FEM analyses were utilized as an input to the Genetic Programming (GP) model. The GP analysis output generates empirical equations for the prediction of track performance with micropile parameters as an input variable. The GP analysis resulted in empirical equations for the prediction of track performance, with the micropile parameters as input variables. The input variables were: length, diameter, spacing, and inclination of the micropile. These empirical equations can be utilized to predict the performance of the track for different reinforcement conditions. The empirical equations by GP have been used to develop a design methodology and a flowchart illustrating the overall design steps is generated. This design methodology can be adopted for practicing civil engineers to perform the upgradation of the existing tracks.

Randhir Kumar Gupta, Sowmiya Chawla
Machine Learning for the Analysis of Equipment Sensor Data in Road Construction Projects

New trends in digitalization in construction have created opportunities for research and informed decision-making. Concepts like digital twins and sensorization have successfully enabled the direct collection of data from construction processes and equipment. For instance, integrating sensors into trucks transporting construction materials facilitates gathering valuable information about the equipment and the surrounding environment. This previously unattainable data can now be utilized to provide pertinent insights into the decision-making process. On one hand, accurate fuel consumption estimations are required to help optimization in construction and transportation infrastructure projects as they represent a major expense. On the other hand, despite the numerous studies conducted to detect cracks and potholes in road pavements, the classification of road types is frequently overlooked. This study aims to bridge this gap by developing a methodological framework that utilizes vibration data from sensors installed in construction trucks to predict the fuel consumption of heavy vehicles and the road category based on the pavement surface quality through which it is circulated. Given their promising results in prior research, the models Random Forest, Neural Network, and Support Vector Machine were applied to the database. The results demonstrate that vibration-based data acquisition methods combined with machine learning algorithms can accurately predict fuel consumption, identify different road categories, and can be successfully applied on a larger scale.

Raquel Silva, Hugo Fernandes, José Neves, Manuel Parente
Data-Driven Pavement Performance Modelling: A Short Review

The road network in many countries holds significant value, and the amount will continue to grow due to the rapid growth of cities and populations. Given this, it is crucial to improve knowledge of the existing road assets, based on which effective management strategies can be customized to maximize the useful service life of current road assets. A key aspect in achieving this goal is the employment of performance modelling, which forecasts future pavement performance. In recent years, the increasing popularity of data-driven approaches has propelled the development of advanced pavement performance models. In this paper, existing data-driven performance models developed globally for different types of pavements and various climate and environmental conditions are first summarized. The review on data-driven performance models then focuses on the capabilities of these models: (i) in handling the time-dependent nature of the data involved, and (ii) in utilizing the existing information available to engineers to forecast future pavement conditions. The objective of this review is to highlight the current state-of-the-art and challenges in data-driven performance modelling and conclude with potential directions and insights for driving innovation and research in the roads sector for practical applications.

Ze Zhou Wang, Abir Al-Tabbaa, Bachar Hakim, Buddhima Indraratna
Random Distribution of Subsoil Damping Ratio and Interval Estimation of Ground Vibration

The damping ratio of site soil is one of the important factors in affecting the propagation and attenuation of ground vibration. Herein, a method is proposed to obtain the random distribution of subsoil damping ratio by field experiment and Bayesian theory. An on-site experiment was carried out and the experimental attenuation curve of the surface wave has been obtained by combining the half power bandwidth method in frequency-wavenumber domain. A priori probability distribution model and the likelihood function of soil damping ratio with depth is established based on the experimental curves. Then, the posterior probability distribution model is further obtained by means of the Monte Carlo Markov Chain-Metropolis–Hastings algorithm, which can be input to Thin-layered soil model to accomplish the interval estimation of ground vibration. Compared with the traditional deterministic prediction, the proposed probability prediction method is more reasonable and has more application value in predicting the environmental vibration induced by rail traffic.

Yanmei Cao, Zhaoyang Li, Jialiang Chen
Short-Term Thornthwaite Moisture Index (TMI) for Australian Climate

Soil-atmospheric boundary interaction is vital for the geotechnical design as the soil behaviour is moisture dependent, especially for expansive soils. Understanding the soil-atmospheric boundary interaction and the effect of climate change can be important for ensuring the resilience of geotechnical infrastructure. Thornthwaite Moisture Index (TMI) has been adopted in many geotechnical designs to account for the climate-induced moisture variations within the soil. The average TMI deducted from long-term climate data (continuous 25+ years) is often correlated with the design parameters such as the suction change depth and hence the characteristic surface movement. The behaviour of many structures can be influenced by shorter-term weather events and a shorter-term TMI may present a better correlation in such scenarios. However, high variability and the non-stationary character of a short-term TMI can be hindrances for any real-life application. This study assesses 1, 3, 6 monthly, and yearly TMI values estimated from 30 years of climate data. The result showed a significant difference exists between these monthly and annual average TMI values. This highlights the significance of incorporating short-term climate events and integrating climate change into geotechnical structures for the betterment of the built environment through safer, more resilient, and sustainable design.

Bikash Devkota, Md Rajibul Karim, Md Mizanur Rahman, Hoang Bao Khoi Nguyen, Claudia Zapata
Modelling of Soil-Vegetation-Atmospheric Boundary Interaction Under Future Climate Scenarios

Nearly 30% of all surface soils in Australia can be classified as expansive. These soils shrink or swell due to changes in moisture content between dry and wet seasons and result in ground movement. Such movements can apply substantial additional stresses to shallow-depth structures like pavements, lightweight buildings, pipelines and other underground utilities. The situation is expected to worsen due to climate change. To ensure structures designed today last their design life and to develop climate-resilient infrastructure, it is important to understand the interactions at the soil-vegetation-atmospheric boundary and how they contribute to ground movement. Numerical simulations can be very effective tools in such situations. This paper discusses an approach for modelling the soil-atmosphere-vegetation interaction and some related challenges. Interactions at an instrumented research site in South Australia were modelled under current and future climate scenarios (years 2050 and 2090). The observations from and limitations of the modelling strategy are highlighted.

Bikash Devkota, Md Rajibul Karim, Md Mizanu Rahman, Hoang Bao Khoi Nguyen, Donald A. Cameron
Towards Safer Roads Post-flooding: Moisture-Induced Pavement Behaviour and Recovery Times

Road infrastructure in Australia is susceptible to aggressive damage caused by excessive rainfall and floods, particularly affecting unbound granular pavements. Annual expenditures by road agencies for rehabilitating damaged roads amount to billions of dollars. Research indicates that maintaining the compaction state of granular materials, specifically Moisture Content and Dry Density, within the dry side of the Line of Optimum (LOO) is crucial for minimizing pavement damage caused by traffic and moisture changes. However, intense rainfall and flooding events can push saturation levels beyond this optimum range. Hence, understanding the moisture variation during and after flood events is of utmost significance. Real-time pavement moisture monitoring employing sensors presents itself as one of the most effective methods to assess moisture variations in pavements during and after flood events. In this study, a road section featuring unbound granular pavement prone to frequent flooding was selected for the installation of moisture sensors to facilitate monitoring. A total of nine sensors were installed, with three sensors per pavement layer, including the base, subbase, and subgrade. Hourly moisture readings were recorded. This paper discusses the moisture variations observed following a flood event, emphasizing the need for a comprehensive evaluation of moisture changes in the post-flood stage to inform pavement management and rehabilitation strategies.

Ayesh Dushmantha, Shiran Jayakody, Yilin Gui, Jinjian Zhong, Anthony Southon, Zachary FitzChance, Chaminda Gallage
Assement of Flexible Pavement Foundation’s Vulnerability Due to Heavy Rainfall in Minnesota

Climate change has led to heavy rainfall in the Midwestern region of the USA causing damage to flexible pavement foundations. This study assesses the vulnerability of pavement foundations to such heavy rainfall in Minnesota. PLAXIS 3D, a geotechnical finite-element analysis tool, was used for creating rainfall scenarios and simulating moisture fluxes in pavement layers. The results obtained from the PLAXIS 3D model were validated using moisture and rainfall data collected from sensors installed at low-volume roads and weather stations at the Minnesota Road Test Facility (MnROAD) of the Minnesota Department of Transportation. The reduced stiffness of the pavement foundation was quantified by reduction of the resilient modulus of the corresponding layers. In this study, the Mechanistic-Empirical Pavement Design Guide (MEPDG) equation was used to predict the resilient modulus of base aggregate at any level of saturation and to determine the effect of moisture changes resulting from heavy rainfall. The analysis showed that greater precipitation intensity increased the moisture flux and reduced the stiffness of the base aggregate layer. A negative linear trend of stiffness reduction due to heavy precipitation was also observed in the analysis. Such reduced stiffness in a pavement foundation could make it more vulnerable to traffic loads and shorten its lifetime.

Md. Jibon, Md. Abdullah All Sourav, Sunghwan Kim, Halil Ceylan
Effect of Wicking Fabric on Frost Heave Suppression of Granular Base Course Material

In cold regions, pavement is susceptible to damage caused by freezing and thawing. Most of this damage is attributed to frost heaving in the subgrade layer, which consists of fine-grained soil. On the other hand, cases have been reported of frost damage in base courses that are considered non-frost susceptible. This is primarily thought to occur because the pavement acts as a cover over the soil beneath the pavement, leading to high humidity in the ground under the pavement. In this study, frost-heaving tests were conducted on unsaturated base course materials using a large-scale freezing test apparatus. This study aimed to investigate frost heaving in unsaturated granular course material and verify a method for preventing frost heaving in unsaturated coarse-grained gravel using a geotextile to drain water from the unsaturated gravel. Based on the results of the heaving tests, frost heave occurred in the soil due to the condensation and freezing of water vapor, which resulted in a frost-heave ratio of 20% despite the material being non-frost susceptible soil. The use of wicking fabric (WF) significantly reduced the amount of frost heave. This reduction could be attributed to the lower water content of the gravel caused by the installation of the WF drainage material, which decreased the soil humidity and created an environment that was less conducive to condensation and the freezing of water vapor.

Tetsuya Tokoro, Tatsuya Ishikawa
Development of Pavement with Road Surface Temperature Reduction and Rainwater Storage Effect

A new combination of permeable pavement and wet pavement was devised to mitigate heat and damage from short duration heavy rainfall. The use of fine sand in the wet pavement base course improved the maximum water storage capacity, and the use of porous glass material improved the capillary rise height. The irradiation tests showed lower surface temperatures for the specimens with higher water content in the lower part of the block, followed by the specimens with impregnated blocks. In the outdoor test, the cross-sectional configuration with higher moisture content in the lower part of the block had the lowest surface temperature. When artificial water supply was applied to the permeable pavement, the surface temperature of the wet pavement was reduced by approximately 2–3 °C.

Ryohei Nagano, Hideo Fujii, Sakura Kakefuda, Koichiro Ibaraki
The Use of High-Capacity Tensiometer for Cyclic Triaxial Testing of Railway Formation Material

The formation layers of railway embankments are often unsaturated and subjected to coupled cyclic traffic-induced and hydraulic loading. Understanding this coupled response requires the development of a testing protocol capable of subjecting soil samples to cyclic loading while continuously monitoring water retention response of the soil. An accurate measurement of the suction variation for the case of repeated cyclic loading is crucial for interpreting the response of the soil considering the principles of unsaturated soil mechanics that are commonly neglected during the design of this infrastructure. In this paper, we present the use of a high-capacity tensiometer of capacity 2 MPa and resolution 0.5 kPa developed at Durham University, capable of measuring suction on the body of soil samples. The setup allowed continuous monitoring of suction at the mid-height of the unsaturated soil sample during cyclic triaxial testing while continuously measuring the volumetric deformations with the help of local displacement transducers. The obtained results indicated that the volumetric compression during cyclic loading reduced the voids ratio leading to an increase in the degree of saturation under constant water content conditions that reduced the soil suction. The obtained results were then interpreted by using mean Bishop’s stress where the permanent strain was consistently found to increase with an increase in the Bishop’s stress ratio. The resilient modulus was also found to be correlated to Bishop’s stress ratio.

Ashutosh Kumar, Arash Azizi, David G. Toll
Impact of Flooding on Pavement Performance Using Integrated Hydraulic and Mechanical Modeling

Flexible pavement structures are vulnerable due to the degradation of unbound materials after water inundation. This study aims to evaluate the resilience of flexible pavements to flooding events using an integrated hydraulic and mechanical modeling approach. Two-dimensional hydraulic models were established to analyze flooding-induced moisture variations in the pavements. Based on the moisture variation results, three-dimensional mechanical models that considered the moisture-stress-matric suction-dependent modulus of unbound materials were developed to calculate critical pavement responses under vehicular loading. The increases in critical pavement responses due to flooding were analyzed and the increase in pavement damage during post-flooding recovery processes was predicted. The proposed methodology provides an effective way of quantifying the impact of flooding on long-term pavement performance.

Xiao Chen, Hao Wang
Metadata
Title
Proceedings of the 5th International Conference on Transportation Geotechnics (ICTG) 2024, Volume 1
Editors
Cholachat Rujikiatkamjorn
Jianfeng Xue
Buddhima Indraratna
Copyright Year
2025
Publisher
Springer Nature Singapore
Electronic ISBN
978-981-9782-13-0
Print ISBN
978-981-9782-12-3
DOI
https://doi.org/10.1007/978-981-97-8213-0