Stopping distance is considered to be an important factor for providing a safe roadway network to the road users. Stopping distance is defined as the distance that the vehicle travels before it comes to a complete halt by considering both, the braking distance and the brake reaction/perception distance. With the emergence of Autonomous Vehicles (AVs), new challenges regarding stopping distance are currently posed to the engineering community. Depending on their total computational power, AVs can have a quicker perception as compared to human beings which can be standardized. AVs need a well-defined process to estimate the braking distance as they significantly lack the experience of human drivers. Current AI technology utilized by AVs incorporates a constant coefficient of friction (µ) for estimating braking distance and applies various factors of safety for unfavourable driving conditions. Such practices can overestimate the braking distance or sometimes underestimate it. Either estimates are not good for a smooth travel of the autonomous vehicle. This is because the stopping distance depends on the fundamental friction relationship between tire and pavement. A research study considering input of realistic tire-pavement friction, signified by the variable coefficient of friction into AVs AI toolkits has never been performed. The absence of research with respect to realistic coefficient of friction for AVs can be attributed to the fact that tire-pavement contact is a complex interaction to investigate and model.
The goal of this MSc graduation thesis is to develop a methodology for investigating the variable coefficient of friction between tire-tread rubber blocks and aggregate blocks utilizing a Tribometer. A Finite Element Model (FEM) of the interaction between tire-tread rubber blocks and aggregate blocks is developed to upscale the empirical findings from Tribometer tests to a large range of variables. The data obtained from the scientific methodologies is suggested to be incorporated into intelligent stopping distance algorithms in AVs AI toolkit to improve road safety. Such research involving Tribometer to study the coefficient of friction between tire-tread rubber and aggregates has never being performed. The study also investigated the hysteretic friction in tire-tread rubber which has never been performed previously. Key aspects of the thesis project included studying the effect of varying rubber temperature and frequency on the sliding friction between tire-tread rubber blocks and aggregate blocks. The research studied the varying effect of rubber temperature and frequency on the coefficient of friction for two surface conditions, dry/wet and three types of aggregates: Bestone, Scottish granite, cement concrete. The FEM results offered a preliminary insight on the investigated variable coefficient of friction. The scientific findings from the model and empirical results show a dependence of thermomechanical properties of the rubber material and surface conditions on the coefficient of friction between tire-tread rubber blocks and aggregate blocks. The findings indicate that the coefficient of friction decreases with increase in rubber temperature, interaction frequency and lubrication at the contact.

The rapid expansion of offshore wind energy has led to an increase in the installation of subsea cables, which are essential for electricity transmission. These cables are typically protected by Cable Protection Systems (CPS) designed to withstand environmental stresses. Although CPS failures are relatively costly and significant, their causes are not well understood. This study developed a hydrodynamic and finite element method (FEM) model to assess the fatigue life of bolts in CPS components. This revealed that waves perpendicular to the CPS cause significant loading fluctuations and identified the entry hole as a potential area for bolt fatigue failure. While the analysis indicated that bolts generally exceed their required fatigue life, the proposed methodology provides a valuable framework for analyzing fatigue in subsea structures. This approach offers insights for improving fatigue assessments and can be extended to other subsea components.

Sustainability has become a paramount concern in modern research, aligning with global efforts to reduce carbon emissions and embrace circular economy principles. The mentioned imperative extends to the field of pavement engineering, where the widening of existing pavements, a common practice to accommodate increasing traffic demands, necessitates sustainable solutions. The current thesis addresses the pressing need for pavement engineering practices that align with ambitious sustainability goals while ensuring structural integrity and performance.

Drawing upon the context of countries like the Netherlands, striving for zero carbon emissions and full circularity by 2030, the research explores avenues for sustainable pavement widening. This involves optimizing designs to minimize material usage, reduce emissions, incorporate recyclable materials, and extend the lifespan of road infrastructure. The challenges posed by non-uniform settlements and stress concentrations at widening joints are investigated, highlighting the importance of accurate material modelling and interface characterization.

Motivated by the need for sustainable pavement solutions, the research aims to guide decision-making in pavement design towards environmental sustainability while meeting functional requirements. The scope encompasses FEM models, EVP behaviour of asphalt surfaces, base layer variations, interface modelling, and comparative analyses between 2D and 3D models.

The current study undertakes a thorough examination of the implications of pavement widening on stress concentrations, material behaviour, and interface modelling, aiming for development of more sustainable and resilient pavement designs. Employing a comprehensive research framework encompassing theoretical modelling and numerical simulations, the study seeks to elucidate the issues inherent in widened pavement structures.

The main thesis objective is the development of an elasto-visco-plastic (EVP) material model, to capture the time-dependent behaviour exhibited by asphalt surfaces under varying loading conditions. Using the Finite Element Method (FEM), the developed material model serves as a foundational pillar for subsequent investigations, facilitating an examination of stress distribution patterns within widened pavement structures.

The Research provides a detailed framework for conducting the study on pavement widenings. It begins with the delineation of study parameters, including cross-sectional geometry, material properties, and simulation techniques. The development and validation of the EVP material model are elaborated, along with the implementation of finite element method (FEM) simulations to analyse stress distributions. Parametric analyses are conducted to investigate the effects of load variations, base layer characteristics, and interface modelling on widened pavement performance. The methodology also includes the utilization of cohesive zone modelling for interface characterization, enabling a more detailed representation of pavement layer interfaces.

The identification of critical stress concentrations emerges as a focal point of inquiry, necessitating a crucial to understand the interplay between load variations, base layer thickness, and material stiffness. Through various numerical analyses, the study seeks to unravel the intricate web of factors influencing stress propagation within widened pavement structures. Moreover, the implications of reduced recessing length and base layers in new pavement designs are subjected to meticulous scrutiny, shedding light on potential trade-offs between structural integrity and resource optimization.


Overall, the current thesis contributes to advancing pavement engineering practices, promoting sustainable transportation infrastructure, and supporting global sustainability goals. Through rigorous analysis and modelling, the research seeks to enhance the understanding of critical factors influencing widened pavement performance, paving the way for safer, more efficient, and environmentally conscious road networks.

Asphalt concrete is the most used material in road construction. Analysing the behaviour and properties of this material is vital for modern day society. Currently, this is mostly done in labs using actual asphalt mixtures. However, this is a very expensive and time consuming process. An upcoming alternative approach is the use of computational models. The finite (FEM) and discrete element methods (DEM) have been used in the past, but both of these have shown significant disadvantages regarding the modelling of discrete particle movement and shape, respectively. An upcoming alternative is the use of physics engines, such as Bullet Physics, to model porous media. This method can have substantial benefits in terms of costs and research time. Also, phenomena can be visualised that cannot easily be observed in experiments.

This research focusses on the utility of Bullet Physics for modelling hot asphalt compaction. Therefore, a complex contact model is implemented which can describe the contact forces of the bituminous mixture. The superpave gyratory compaction process is digitally modelled. The model has been programmed in PyBullet, an open source physics engine, programmable in Python. A parametric study has been performed, which reveals the significance of certain properties, which cannot easily be investigated during laboratory compaction.

Bullet Physics was not initially designed for scientific research in the field of structural engineering. Therefore, alterations are needed to make the software usable. The implementation of a complex contact model is challenging. Although the Burgers’ contact forces can be correctly described, it proves difficult to implement custom contact forces directly in PyBullet. Two attempts have been made. In the case of a custom integration scheme, the computation time proved too long to be applicable for large scale simulations. In case of a direct implementation in Bullet Physics with the application of external forces, instability occurs. The only correct way to implement a custom contact model is by altering the source code.

During the simulations with a simpler contact model, substantial improvements of digital simulations over actual experiments are presented. The consistency proves far better than the prescribed minimum. The influence of inertia and friction can be assessed. It turns out that inertia, as well as the friction of the mould, can be disregarded. Another phenomenon that can be clearly illustrated is the revolving of aggregates inside the mould. Further analysis has shown that the average contact area depends on degradation, but in a typical asphalt mixture does not depend on the size of the individual elements, and could rather be treated as a constant. Further analysis shows that Bullet Physics is incredibly efficient, thus yielding the possibility of performing large scale simulations within reasonable time.

The Netherlands is a northwestern European country. The location makes the Netherlands an important entry for trade to the European continent resulting in high traffic intensities [1]. The Netherlands has one of the densest road networks in the world, having over 139.000km of roadways [2, 3]. The understanding of the link between the functional properties and the behavior of the road is crucial [4]. However, predicting such properties is still challenging, and expectations differ from the results found in the field [5, 4]. Data-driven approaches have been part of pavement engineering for decades and have shown to be a powerful tool for performance prediction [6]. This thesis aims to develop a machine learning framework for predicting stiffness, fatigue resistance, resistance to permanent deformation, and water sensitivity. Along with sensitivity analysis to understand the deviations from expectation. The objective is to develop a framework to understand the applicability of machine learning tools and the impacts of the composition parameters into the mix. The models developed applied three different machine learning tools for regression: Support Vector Machine, Random Forests, and Gradient Boosting. The models were compared to a statistical model to validate the work. The statistical approach was a Multiple Linear Regression. All developed models used the database from the NL-LAB project. The machine learning models were compared, and the best-performing proceeded to a sensitivity analysis. The sensitivity analysis used SHAP values, which derive from the Games Theory and have shown to be powerful tools for complex model interpretability [7]. The models had good accuracy prediction. For most properties, machine learning had a higher performance than the statistical model. Gradient Boosting performed the best from the machine learning tools and was selected to finalize the research. The sensitivity analysis had good results, confirming some of the expectations and setting a precedent for researching others.

Because of the sustainability transition in the asphalt industry, it is increasingly popular to apply reclaimed asphalt pavement (RAP) instead of new asphalt. Then, less new material is needed and the amount of waste products is reduced. However, there are questions regarding the performance of asphalt mixtures with high RAP percentages, particularly in terms of potentially excessive high stiffness and a decrease in workability. In the Netherlands, a RAP content of 60% in combination with a softer binder is common practice, but an increase in RAP content means that rejuvenators are often required.

The research objective was to study the multi-scale material properties of hot mix asphalt mixtures with high reclaimed asphalt pavement percentages using micromechanics. At binder level, the DSR test was done and it was also used to select and reject the rejuvenators which are applied at mixture level. The chosen rejuvenators, Neomex HR and Cecabase RWI, were applied to the mixtures with 80% RAP. Besides that, a reference mixture was tested with 65% RAP and a reference mixture of 80% RAP. Further, the results were used to fit a modified micromechanical Hirsch model for RAP mixtures with rejuvenators.

Based on the material tests and comparing it with the reference (65% RAP mixture), it turned out that there is potential to implement 80% RAP mixtures. Both moisture sensitivity and the rutting resistance are improved when a higher RAP content is applied, while the total fracture toughness has remained the same. The stiffness of the 80% RAP mixture is substantially higher, but it is still applicable. The biggest concern is the fatigue resistance, because the fatigue resistance of the 80% RAP mixture without rejuvenator is worse for higher strains.

The application of the rejuvenating additives led to the properties of the 80% mixtures being more similar to the 65% RAP mixture and the workability is improved. However, it did appear that the dosage of the additives is on the high side. The stiffness is lower than the reference mixture, especially the mixture with Neomex HR. Also, the rutting resistance is lower than the reference mixture, especially the mixture with Cecabase RWI. Future research may optimize the dosage to improve results.

The micromechanical Hirsch model provided stiffness results with a reasonably accuracy, although this model used general model coefficients. The simplified Hirsch model with inclusion of the factor Pa could not be simply adopted when it is fit for other types of mixtures. The predicted stiffness for all three mixtures was much higher than it actually is. The adjustment of the fitting parameters, according to the laboratory results of the 80% RAP, led to accurate results making the simplified Hirsch model with inclusion of the factor Pa applicable to the mixtures with 80% RAP and a rejuvenating additive.

Maintenance is a necessary measure to keep the asset, in this case a road network, in good condition. Spending too much on maintenance is clearly not efficient, while not spending enough may cause the condition to drop below a desired value; moreover, it will almost always cost more to correct the emerging damages afterwards.
In this report, a moving horizon optimization approach is presented as a conceptual model to improve the efficiency of maintenance of a road network, compared to the currently used maintenance approach.

Pavement structures are assessed based on its functional and structural capacity in order to evaluate the safety of its use. Structural pavement evaluation is often carried out by road authorities or consulting companies that use non-destructive testing (NDT) methods to assess the remaining life and structural capacity of a road structure at project or network level. In Western Europe and in most parts of the world, the most common equipment used as part of the NDT method is the use of the Falling Weight Deflectometer (FWD). The FWD is able to capture deflection values that can be used for the backcalculation of moduli and to determine remaining life of a structure by mechanistic-empirical equations.
In the Netherlands, like in many other countries, motorways have an increased number traffic flow based on high population densities accumulating in major cities. Due to this, pavement evaluation with stationary equipment such as the FWD can become relatively expensive due to the disruption of traffic. To solve this problem, research institutions and consulting companies have developed different versions of continuous evaluation equipment that are able to perform the structural analysis in a similar way to the FWD. In 2018, Dynatest® launched the Rapid Pavement Tester (RPT or RAPTOR) which aims to perform functional and structural evaluation of road networks at traffic speed. Continuous evaluation devices such as the RAPTOR are in constant development in order to achieve the quality and guaranty of use that equipment such as the FWD have in the pavement engineering industry. Currently this type of device uses technology that is less accurate than the sensors (Geophones) used in FWD equipment. This becomes a significant impediment in the evaluation of high stiffness pavement structures as the calculated deflections are in the lower end of the spectrum. This research aims to find a methodology that can be used in order to find limitation stiffness parameter values for which it is viable to use continuous evaluation equipment. Additionally, this research aims to find a method that can be used in the pavement engineering industry and research that is able to aid the data collection process of pavement layer information by means of machine learning tools. This was a problem faced during the elaboration of this research as the data collected to perform the analysis was incomplete in terms of layer thicknesses information. This process is carried by means of an artificial neural network (ANN) that is able to predict layer thicknesses and moduli based on deflection values and deflection parameters that obtained with the FWD.
The analysis of this research is carried by comparing deflection values in different road networks collected with the FWD and the RAPTOR under similar weather conditions, where the predicted layer information is used to assess the cut-off values and limitations that the current version of the RAPTOR has when compared to the FWD. From the presented results it was found that the asphalt layer modulus showed the highest correlation to the limitation values where the RAPTOR is able to present reliable results when compared to the FWD. Additionally, promising results were found in the use of ANN method to predict missing layer information which are assumed to improve with a specific build in the ANN architecture.

The fatigue life of asphalt concrete is an important parameter in the functional design of a road construction. In the Netherlands it is determined by the four Point Bending (4PB) test on laboratory prepared specimen. During the past years there is an increase in desire to validate the laid down asphalt pavement to the established functional properties. The most practical test setup applicable is theCyclic Indirect Tensile Test (CY-ITT). Both fatigue tests differ on many fronts from each other. The resulting traditional fatigue line between them is therefore not coherent. A solution is previously sought in applying the energy based method of the Ratio of Dissipated Energy Change (RDEC). Developedby Shen & Carpenter. This research is a continuation of those works. The objective of this research is therefore formulated as follows: Establishing a fatigue life relation through the energy methods, which couples the fatigue life results of both the CY-ITT and 4PB fatigue tests and preserve the asphalt mixture characteristics.In total 12 different mixtures were used to establish a wide field of mixture variation. Differences in RAP content, bitumen, PEN-grade and max aggregate size were used. Each mixture was tested on both force controlled CY-ITT and displacement controlled 4PB. Additional 4PB force controlled and Uni-axial displacement controlled fatigue tests were conducted on a single mixture.The results were analysed by the two main energy methods: The RDEC and the Viscous-Elastic Continuum Damage (VECD) model of Kim. The RDEC method is based on the slope ratio of the dissipated energy. The dissipated energy is calculated by the hysteresis loop. The VECD method is based on thelinearization of the strain by the pseudostrain energy function. The rate of the pseudostrain energy function (GR) is then used for the fatigue relation.For the RDEC is concluded that the method is a single line relationship. Independent of frequency, temperature, mixture type, mixture density and stress strain relation for each mode of loading. The test setup independence between the CY-ITT and 4PB was found for 6 out of the 12 mixtures. The 4PB force controlled test did not deliver an evident result. The Uni-axial displacement controlled fatigue test validated the RDEC independence of test setup. It is therefore not recommended to use the RDEC in the current configuration as a practical application for validation between laboratory prepared specimen and the road constructed asphalt concrete layer.For the VECD is concluded that the CY-ITT and 4PB did not form a single coherent fatigue line in the log(GR − NfatSN) relation. The relation is for a single test setup however mixture type dependent. The hypothesis that the fatigue line is dependent on the linear stiffness from the frequency sweep and could be shifted similar as on the mastercurve, proved preliminary to be true for the same test setup. However to be untrue between the CY-ITT and 4PB GR fatigue relation. The used relation between log(GR−NfatSN) is therefor not applicable in linking the CY-ITT and 4PB fatigue test setups.

Pavement undergoes a fast deterioration process either due to the damages induced by weather conditions, an increase in traffic flow and load, or passive factors like aging of infrastructure. Thus requiring periodic rehabilitation measures to maintain the condition of the underlying asset. Since damages on the asphalt road, impose economic setbacks and concern for the users, governmental authorities are looking for a proactive approach to detect and classify distresses in their "early" stages. As a reason, governmental authorities like the Province of Zuid-Holland (PZH) yearly inspect the road network, which was optimal until now, but as the traffic flows are increasing and weather conditions are worsening, a new approach is required to mitigate the need for a frequent, cost-effective and reliable inspection method. Modern data sources such as smartphones are the biggest data generators. Having an intriguing number of sensors and in-built features, governmental and private authorities are just starting to acknowledge the potential of such crowd-sourced data generators. In this research data-types like vibration and imagery were gathered and synthesized to assess the efficiency and accuracy of the 3 data-driven models. A 7-step methodology was implemented to build all the machine learning models. At first, vibration data was gathered to detect road anomalies and predict the International Roughness Index (IRI), by building a Random Forest decision tree. Secondly, a Convolution Neural Network (CNN) was constructed and utilized to classify pavement surface distresses. The last model, is a next step towards autonomously distinguishing the classified distress with its severity ranking. A Deep Neural Network (DNN) called EnDec (Encoder & Decoder) architecture was built and trained on the Dutch supercomputer called "cartesius", by utilizing multi-threading opportunities, to objectively segment the given pavement surface distress.

An ideal road management system requires a real-time assessment of pavement performance. But there are many root causes of the status quo that the expectation and the system have not been achieved yet. One of them is the monitoring system. In the Netherlands, the measurement is carried out once a year because of the budget and also because it is time-costly. To set up a real-time assessment system of pavement condition, this thesis proposes to use the real-time traffic data as the indirect way of monitoring the road, and applies three performance models to verify the correlation between traffic flows and road performance. The applied models are the regression models, the survival model, and the decision tree classifier. As a result, according to the test data of A15 in the Netherlands from 2015 to 2018, all the models indicate that the traffic characteristics influenced road performance, particularly, the traffic flow could worsen the pavement condition which already performed badly. The model result confirms the feasibility of establishing a real-time assessment tool of pavement condition by using the traffic data as the monitoring method. The specific establishment process of the tool is designed in the final.

Road damage detection is important for road safety and road maintenance planning. Road surface anomalies, like potholes, cracks and ravelling, affect driving conditions, such as driving comfort and safety, noise emission, load loss of trucks, increase of fuel consumption and traffic circulation. Locali- sation of these anomalies allows for targeted road maintenance, which contributes to the improvement of driver safety, comfort and the optimisation of road maintenance.
The current technique to detect road damage is that road inspectors determine road damage in road images. However, the results are susceptible to human subjectivity. An improvement on image based road damage detection is using LiDAR data, because the geometry of road damage is measured too. To mitigate the issue of human subjectivity, an automated method for road damage detection was developed for the profile laser scanner on the IV-Infra car. This laser scanner is mounted at the back of the vehicle so that its profile lines are perpendicular to the driving direction. The proposed method consists of: (I) feature extraction with a sliding window algorithm; (II) K-means clustering to create training data; (III) Random Forest classification and (IV) morphological operations to remove noise and identify larger damage patches. This method was tested on an 800-meter long provincial road with different road defects and road types. Most occurring road damages are cracks, craquel and raveling. The results of this method were validated in two ways: using a road inspectors damage classification and a custom-made validation set based on orthophotos. An overall accuracy of 73% is achieved for the fully automated process. When training of the Random Forest was based on an improved, semi-automated training data, the overall accuracy was 58%, this gives visual clear results. This is explained by more noise are presented in the results based on the fully automatic method, which is overlapped with the coarse road inspector’s data. Optical inspection shows that the semi-automated method identified almost all damages of the custom-made validation set, although a shift between the point cloud and the validation is found. Still, the method has some difficulties with detecting the transverse cracks. This problem can be solved by integrating the two other mounted laser scanners of the Iv-Car, but pre-processing is needed to organise the point cloud. Also, an improvement in georeferencing the validation data would help to optimise the method and training data. Nevertheless, promising results are achieved by this method.

The adaption of the functional characterization of asphalt concrete mixtures by the Dutch authorities, led to a big necessity in acquiring a fundamental understanding on its properties. In order to achieve this, the long-term project NL-Lab was initiated by InfraQuest in 2012. With the advantage of its longevity and the multiple parties contributing with material, testing and analysing, the knowledge aimed for, is gradually accumulating with the time passing. After 5 years of running, a considerable number of data have been collected and analysed, leading to many observations, that in turn raised questions and doubts. These doubts are the subject of this research, focusing on the functional characteristics of water sensitivity and permanent deformation.
A common problem for both properties regards the production and compaction of type-testing specimens. The practice so far suggests both stages to take place in laboratory conditions. However, there were indications that the performance of the same asphalt mixtures taken from the field instead of the lab, differed. Testing and comparing in terms of water sensitivity and permanent deformation, these indications were indeed repeated. Lab and field specimens did not match in performance in the majority of the tests, suggesting that differences between the two conditions are projected in differences in performance.
The second common problem also evolves around the type-testing, this time regarding the test procedure as a whole. It is a highly time and money consuming process that has to be repeated in a trial-and-error manner, until a satisfying mixture in terms of performance is found. The aim of this research was to limit this process as much as possible by replacing it with regression relations. For the water sensitivity test, the relations obtained were of a fairly good predicting quality, and could be eventually used in the preliminary mixture design. The permanent deformation relations however, were at low levels of predictability, due to the test’s high sensitivity in being reproduced and the inconsistent test setups used by the various contractors taking part in the project.
Focusing only on water sensitivity, the conditioning protocol described in the current standard, is considered as not very capable of capturing the property it aims for, and that it is conservative in the damage it causes to the mixture. Utilizing two conditioning protocols, MIST and Freeze-Thaw protocol, the standard method indeed was found to be the least damaging of all. With the MIST being the most destructive, at extreme levels in most cases, and the Freeze-Thaw following it, each method’s pros and cons were found.
The last aspect tested referred to the friction reduction method used during the permanent deformation test (triaxial cyclic compression). The old and new standard versions both prescribe two different materials, while the requirements for rutting remained unchanged. Testing using these two materials, Teflon and Latex, plus one new, it was found that the differences in the triaxial test output were major. Consequently, this mismatch means that inconsistent comparisons take place, leading to conclusions on rutting performance not representative of the actual situation.