dataesr/scientific-paragraphs-categorization huggingface dataset:
Ultimately, the research team incorporated the ESAL values into the ME pavement design software for comparative analysis of service of life of pavements for the aforementioned three sets of traffic data. Climatic data from an adjacent weather station, as well as performance criteria limits, were also assigned using the agency defined thresholds. The analysis results were contrasted with the steady rolling conditions which serve as the benchmark in this study. Therefore, the difference between the service life analyses represents the reduction of the service life associated with the acceleration/deceleration of the SHL vehicles. Additionally, "pavement life reduction" index was calculated to provide a quantitative measure of the severity of the distresses imparted by acceleration/deceleration of the SHL vehicles in the network as: where, PL steady rolling , PL acceleration , and PL deceleration represent the expected pavement life considering the steady rolling, acceleration, and deceleration conditions of the SHL tires, respectively.
Finite element modeling In this study, ABAQUS FE program was used for determination of the SHL-induced pavement responses under various tire rolling conditions, with considerations of the viscoelastic nature of Asphalt Concrete (AC) layer, complex traffic loading conditions, and tire-pavement interactions. Subsequent sections provide the relevant information on the 3D FE models developed in this study for simulation of the pavement structures, material behavior models, SHL tires at acceleration/deceleration conditions, and tire-pavement interactions. Detailed information on input parameters including material models, material properties, and traffic characteristics are provided in  Ashtiani et al. (2019).
ANALYSIS AND RESULTS In this section, the first three mill-and-overlay cases in Figures  1a-1c  are analysed. The analysis is repeated twicewithout and with the inclusion of grid reinforcement. Because Case (d) consists of full removal and repaving of the AC layer, it serves as a reference. Assuming that damage and ageing effects are only confined to the AC layer, Case (d) also represents the pristine mechanical condition of the pavement system. Ultimately, the reinforced cases, unreinforced cases, along with the reference case amount to a total of seven different pavement systems. Calculations were done utilising the LET code ALVA  (Skar and Andersen 2020; Skar et al. 2020 ) that was extended to include the FL formulation from  Levenberg and Skar (2020). ALVA is an open-source LET kernel code based on the numerical computing package MATLAB.
Subsequently, a logical performance model was proposed, calibrated, and further validated using 64 pavement sections lasting in service for 85 years, on average. 80% of pavement sections (51 sections) were used to calibrate the model while the remaining 20% (13 sections) were used for cross-validation purposes. Figure  1  shows the methodology for the development and calibration of the predictive model for the estimation of the fatigue performance of cement stabilized virgin and reclaimed base materials in flexible pavement structures. The process includes three major areas that will be discussed in detail in the upcoming sections: Development of Fatigue Performance Model, Field Data Collection and Analysis, and Field Calibration and Validation Algorithm. Relevant databases were collected based on laboratory test results, finite element simulations, and field testing for each subcategory illustrated in Figure  1.
Where: N f is the number of load repetitions to failure, ε h =ε IDT is the strain ratio, α 1 and α 2 are laboratory calibration factors, and β 1 and β 2 are field calibration factors. The dynamic IDT results were further used for the determination of two sets of laboratory shift factors, α 1 and α 2 for virgin and reclaimed materials. The authors believe it is prudent to separate the databases for virgin and reclaimed materials as their mechanical behavior was observed to be drastically different in the laboratory. Based on the curve fitting procedure adopted in this study, the laboratory shift factors for virgin aggregates, α 1 and α 2 , were 0. 4561 and 0. 026, respectively. For reclaimed materials, the model parameters α 1 and α 2 , were 0. 7163 and 0. 043, respectively. Section 5 of this paper provides the procedure for the estimation of the field calibration parameters, β 1 and β 2 , values.
Field data collection A comprehensive database of cross-sectional characteristics and material properties using nondestructive testing equipment, such as FWD and ground penetrating radar (GPR), was compiled from 64 pavement sections. The evaluated pavement sections covered a broad range of site characteristics. The authors combined the field database with available databases from published relevant literature. The combined dataset was incorporated into the calibration algorithm and cross-validation process of the fatigue performance model. The database required for executing the calibration and validation of the model included the location of the pavement sections, layers thickness (based on design blueprints and GPR results), backcalculated FWD modulus, materials properties of stabilized base layers (i.
Pavement response calculations by Finite Element (FE) method To simulate field conditions and calculate critical pavement responses, the authors used the FE software Abaqus to model the 64 pavement sections. Different layer configurations of pavement structures were simulated in the FE program. Figure  3  (a) exhibits an example of meshing, boundary conditions, geometry of layers, and traffic-induced tensile strain results for a representative pavement section that contains an asphalt concrete surface layer (AC), cement treated base layer (CTB), and the subgrade soil (SG). Site-specific structural properties of pavements were assigned to each representative field roadway section in the FE model. Since the entire three-dimensional model was symmetric, only a quarter size of the structure was simulated.
FIELD CALIBRATION PARAMETERS AND VALIDATION ALGORITHM To provide credibility to the structural design process of pavements with cement treated base layers, the study incorporated a field calibration process for the novel fatigue performance model. The split-sample approach was used to verify the accuracy of the prediction model during this calibration effort. Approximately 80% of the sections (51 field pavement sections) were randomly selected for field calibration purposes and 20% of the sections (13 field pavement sections) were selected for cross-validation. Laboratory calibration factors (i. e. , α 1 and α 2 ) of the fatigue performance model were adjusted to improve precision using the representative field database consisted of 51 pavement sections. The predicted strain ratios in the laboratory were compared to the strain ratios obtained from pavement sections based on the collected field database for stabilized virgin aggregates and stabilized reclaimed materials.
More details on the parameters included in the database are provided in  Ashtiani et al. (2020). Figure  6  presents the initial relationship between predicted and measured strain ratios before calibration. Field calibration factors (i. e. , β 1 and β 2 ) were introduced in the fatigue performance model to eliminate the bias and reduce the standard error between the predicted and the measured strain ratios. Figure  7  shows the final relationship between predicted and measured strain ratios considering calibration factors. As observed in the plot, bias was eliminated and the precision was improved for the developed fatigue performance model. Based on the comparison of strain ratios, 83% of predicted strain ratios had a difference lower than 5% when compared to the measured strain ratios for stabilized virgin base layers.
Similarly, 80% of predicted strain ratios had a difference lower than 5% when compared to the measured strain ratios for stabilized reclaimed base layers. Laboratory and field calibration factors of the model for different cement stabilized materials are tabulated in Table  1. The results clearly show improvements in the predicted values after incorporation of the field calibration values. As stated earlier, the field calibrated model was cross-validated using an independent set of data (20% of the field dataset) to evaluate the predictions. The validation results are shown in Figure  7. The close scatter of the validation data cloud around the equality line is an indication of the capability of the model to provide a reasonable agreement between the prediction and field conditions. Additionally, the maximum differences between predicted and measured strain ratios were 6.
All four aggregate sources were stabilized with 2%, 3%, and 4% cement. Therefore, the model incorporates both lightly stabilized systems, as well as heavily stabilized aggregate matrices. The primary divergence from existing fatigue performance models in this study was the departure from using modulus of rupture and incorporation of the IDT test and the shrinkage strain for the estimation of fatigue life of pavements with cement treated base layers. The study also aimed to calibrate and cross-validate the proposed fatigue performance model based on field performance data. This objective was achieved by developing a comprehensive catalog of cross-sectional features and material properties of 64 pavement sections based on non-destructive field testing, such as FWD and GPR, in combination with available databases from published pertinent literature.
Backcalculation analysis The FWD deflection values were backcalculated using Evaluation of Layer Moduli and Overlay Design (ELMOD) software to determine the stiffness of the pavement layers. ELMOD performs layer elastic analysis to iteratively adjust the seed modulus for each layer to match the predicted and measured deflections within some tolerable error. To start the backcalclation analysis, pavement layer thickness and seed moduli of pavement layers should be defined. The thickness of the pavement layers of four sections was determined from the field cores conducted during the construction of the four sections. The asphalt layer was treated in the backcalculation analysis as two individual layers; the top layer is a thin asphalt overlay with a seed moduli of 3447 MPa at 20°C determined based on the dynamic modulus master curve conducted in the laboratory.
The backcalculation was conducted using the ERAPave software  (Erlingsson and Ahmed, 2013). The subgrade is divided into two layers, where the boundary between the layers is the GWT level in phase w2. The subbase is divided into three layers, where the lowest layer is below the GWT in phase w3. The backcalculated deflection results are compared in Figure  3. The results are based on 50 kN falling weight load. As expected, increased GWT and moisture content resulted in increased deflection and reduced stiffness of the pavement materials. This tendency is reversed when GWT is lowered. Although the 0/90 mm structure originally shows lower deflection and higher subbase stiffness, this structure is more affected by the moisture changes and shows higher deflection than the 22/90 mm structure in phase w2 and w3.
OBJECTIVES In this paper, we present the use of Basis Pursuit [BP]  (Chen et al. , 2001)  on dense TSD deflection slope measurements to remove unwanted noise and extract the pulse responses (peaks in deflection) produced by weak locations, such as weak joints in jointed pavement networks. We aim at decomposing the TSD measurements into a combination of continuous components plus pulse responses due to the weak spots. BP is an objective, reproducible, and computationally fast methodology to achieve this task, even with large input datasets. Thus, it is suitable as a framework to easily detect weak spots within a pavement segment that was surveyed at a high resolution. The remainder of this paper is divided into three sections. Firstly, we briefly present BP as a signal processing technique, relate it to the Least Absolute Shrinkage and Selection Operator [LASSO]  (Tibshirani, 1996; Chen et al.
,2001; Hastie et al. , 2009) , and discuss practical issues related to the selection of its hyper-parameters. Following, we present a case of signal denoising with BP using a simulated dataset. Finally, we present the results of BP denoising of actual TSD signals from different backgrounds: an experimental test track and an actual 3 survey of overlaid jointed pavements. This paper contains an abridged version of a recently published article  (Scavone et al. , 2021) , yet the intention of this paper is not to dive too much into the mathematical and computational detail behind BP, but actually to focus on the applicability as a denoising technique for TSD measurements, providing an example of an actual TSD survey on composite jointed pavements that we denoised with BP. A BRIEF OVERVIEW OF BASIS PURSUIT Basis Pursuit [BP]  (Chen et al.
(2021). Instead, it has been approximated using the robust estimator presented in  Katicha et al. (2015) : A Matlab implementation of BP that processes each segment separately and extracts the recovered pulse components and their geographical coordinates was written for this case study. Processing the four segments required roughly 3 minutes on an average-spec personal computer. Figure  5  below shows the denoised TSD signal and the recovered pulse responses for one of these segments, both using the optimized penalty and the Universal Threshold. The recovered pulse components were exported to a GIS application (QGis 2. 18) for easy visualization and interpretation. The map in Figure  6  presents the detected weak spots with an amplitude greater than 300 microns for the EB and WB segments between MM 52 and 54 for the reconstruction with optimized λ Those weak spots also detected by the BP decomposition with the universal threshold are highlighted with a circle.
With the TSD it is possible to measure the motion of the concrete slabs, as the loaded axle moves from one slab to the next. This motion is conveniently quantified via the difference in Doppler velocity in front of the load and behind the load Δv ¼ v þ v. For the TSD used in this measurement the v þ laser is located 13 cm in front of the load, and the v laser is located 20 cm behind. The runway was measured with 6 passes of the TSD and the data reported in 5 cm intervals. In Figure  7  (a) the velocity difference Δv is plotted along one of the measurements. In Figure  7  (b) a zoom of the same measurement is shown. Spikes in Δv are seen at regular intervals, corresponding to the locations of the slab joints. In most of these places the slabs and joints are covered with asphalt, but the joint movement is still clearly visible through the asphalt.
For both of the afore-mentioned purposes during pavement evaluation, the major engineering challenge lies upon using tools and analysis theories that lead to reliable results and conclusions in respect to pavement condition. Commonly, data collected through Non-Destructive Testing (NDT) systems, including the Falling Weight Deflectometer (FWD) and the Ground DOI: 10. 1201/9781003222880-43 Penetrating Radar (GPR), is used as input to back-calculate the pavement stiffness profile and proceed with a response analysis that enables pavement life expectancy estimation  (Plati et al. 2020b). The Multi-Layered Elastic Theory (MLET) is most often used as the background theory of the previous analysis steps  (Marecos et al. 2017 , Crook et al. 2012). However, there is enough evidence that the Asphalt Concrete (AC) materials exhibit viscoelastic behavior, since they are temperature-and loading rate-dependent  (Grellet et al.
No matter how well or not a pavement has been designed and constructed, this is expected to be serviceable for a long period beyond its design life. As such, formulating a method for the as-built pavement condition assessment contributes to a database development in order to systematically monitor pavement performance in the future and make a rational scheduling of potential maintenance or rehabilitation actions in the long term. Nevertheless, additional research is needed with a wider material spectrum and NDT data to further assess the feasibility of the proposed approach.
Based on the above, the main objective of the presented study is to test a solution that can be used to make smarter the road pavement monitoring. In more detail, an urban site investigation was carried out using as source of noise and vibration a FWD and a LWD, and an innovative vibro-acoustic sensor as receiver of the response of the road pavement to wellknown loads produced by the sources cited above. The characterization of the different road sections investigated (damaged and un-damaged) was carried out through a GPR and a FWD, which were used for providing reference data of pavement bearing capacity. In the paper, tests and results in selected trial sites are used to identify Strengths, Weaknesses, Opportunities, and Threats (SWOT analysis) of the application of smart vibroacoustic sensors for the assessment of pavement residual life.
Transmission and reception of radar pulses are performed from one or more antennas that are moved on the investigated medium. The collected data are processed and saved on a control unit, that is also used to generate the necessary pulses for the operation of the antennas. (Saarenketo, 2006). As mentioned GPR can provide a fast, nondestructive measurement technique for evaluating asphalt layer, presence of infrastructural interferences within the pavement, or to assess infrastructural conditions  (Goulias et al. , 2020). Thickness of the asphalt layer can be calculated by two methods: two-way travel time or the common midpoint method  (Lahouar et al. , 2002)  when multiple GPR channels are available. Since urban pavement present thin layers, it is often challenging to differentiate the reflection from the top and bottom of the layer.
Antennas with a center frequency of 2000 MHz can provide a sufficient resolution to measure a minimum layer thickness less than 2. 5 cm (1 in. ) with an accuracy of 0. 25 cm (0. 1 in. ) (ASTM D4748). Moreover, when GPR approaches above a pipe, a cable duct or a manhole, the two-way travel time versus travel distance has a parabolic shape  (Al-Qadi et. al. , 2005) , so it is possible to highlight the presence of these types of interferences. The GPR used in the present study uses two antennas with 600 MHz and 2000 MHz frequencies. The K2_FW acquisition software is used to manage the phases of radar acquisition and to review the data acquired directly on site. The default field propagation speed is equal to 10 cm/ns.
i. e. , the vibro-acoustic response of pavement to loads. In more detail: 1) The vibrations are produced by the vehicles (source) that propagate into the road layers. 2) The corresponding variation of air pressure is detected by a microphone placed inside a sound insulating coating. More precisely, the acoustic receiver consists of sound isolated microphone + external sound card + laptop for recording, processing and analyzing the received data (Figure  3 ). Importantly: 1) The road pavement is considered as an acoustic filter with a given SHS. 2) The method aims at detecting hidden cracks into the road section (e. g. , under the wheel paths), while they are damaging the "acoustic filter" and before they arrive to the road surface. 3) The worsening of the SHS of the road pavement (filter) is recognized using the variation of meaningful features extracted from the recorded signals (vibro-acoustic signatures of the monitored pavement), in such a way as to obtain an efficient and intelligent solution.
Note that the receiver shown in Figure  3  (used in this study) is one of the possible systems that can be used to the purposes mentioned above, and that it is being improved (seeking to obtain a self-powered wireless system) in order to address some of the objectives of the ongoing Italian project USR342-PRIN 2017-2022. In this study the following devices were used as receiver (measurement chain): 1) Omnidirectional pre-polarized microphone "Audix TM1" (frequency response: 20-25 kHz ± 2 dB, sensitivity: 6 mV/Pa at 1 kHz, dynamic range: 112 dB), acoustically isolated using a plastic box filled with isolating material and modelling clay (used also to attach the box to the road). 2) External sound card "Roland quad-capture UA-55". 3) Recorder/Analyzer: ASUS ZenBook (model: UX433F, Intel® Core™ i7-8565U, RAM: 16 GB, 64bit x64) running MATLAB codes that allow recording acoustic signal with a sampling frequency of 192 KHz.
URBAN ROAD TRIAL TEST In order to identify the cracked and uncracked areas to be investigated during the urban road trial test and the thickness of asphalt layer in these points, the Ground Penetrating Radar was used. About 300 meters long an urban road in Catania (South Italy) were covered in both directions, for a total of about 600 meters. Thanks to the K2_FW acquisition software, the thickness of asphalt layer was analysed along the entire path with a step of 1 meter and the presence of any underground services was identified. Only the areas in which the asphalt layer with uniform dense mix was thicker than 15 cm were taken into account (Figure  4 ). Through a visual inspection, among the areas having a similar thickness greater than 15 cm, four sections were identified, two without surface cracking and two with cracks (Figure  5 ).
ACKNOWLEDGEMENTS AND FUNDING The authors wish to acknowledge  Leif Söderberg  ( NCC industry AB ) who was hired to take the drilled cores samples. Also acknowledged are Ramudden AB which were hired to keep us safe during the acquisition of the drilled cores. Andreas Waldemarson  ( VTI ) is acknowledged for performing the traditional laboratory measurements. This study was carried out as a master thesis project at  KTH. The drilled cores acquisition was funded by  Linköping.
ACKNOWLEDGEMENT The work reported here is part of the project " CRABforOERE -Cold recycled asphalt bases for optimized energy & resource efficient pavements " funded by the  CEDR Transnational Road Research Programme Call 2017 : Materials funded by  Austria ,  Belgium-Flanders ,  Denmark ,  Germany ,  Netherlands ,  Norway ,  Slovenia ,  Sweden  and the  United Kingdom ACKNOWLEDGEMENTS  This study was supported by the  Latvian Council of Science  on the basis of the  Cabinet of Ministers of the Republic of Latvia Regulation N• 725 "Evaluation of Fundamental and Applied Research Projects and Administration of Finance " within the project:  N•LZP-2018/ 1-0237  " Innovative use of industrial by-products for sustainable asphalt pavement mixtures ". The authors gratefully acknowledge  SLLC "Latvian State Roads " for cooporation in obtaining laboratory results.
ACKNOWLEDGEMENTS This research was supported by the  Iowa Department of Transportation  through  IHRB  Project  TR-721. This support is gratefully acknowledged. The findings and opinions expressed herein are those of the authors and do not necessarily reflect the views of the Iowa DOT. The authors wish to thank  County Engineers Nick Rissman ,  Nicole Stinn ,  Jacob Thorius , and  Brandon Billings  as well as their crews for assistance in construction and maintenance of the test sections. The authors also thank the representatives of the slag source and the sand company providing the clay slurry for overseeing and providing input during construction of the related test sections.
ACKNOWLEDGEMENTS This study was sponsored under the research project " FE 08. 0248/2016/CGB  " Asphalt surface course on continuously reinforced concrete pavement; scientific monitoring of the test sections in composite construction during the operating phase ", these are being investigated by the  RWTH Aachen University  together with the  German Federal Highway Research Institute (BASt). The authors would like to thank the  German Federal Ministry of Transport and Digital Infrastructure (BMVI)  for supporting this research.