Stiffness characterisation of full-scale airfield test pavements using computational intelligence techniques

The falling weight deflectometer (FWD) is a non-destructive test equipment used to assess the structural condition of highway and airfield pavement systems and to determine the moduli of pavement layers. The backcalculated moduli are not only good pavement layer condition indicators but are also necessary inputs for conducting mechanistic based pavement structural analysis. In this study, artificial neural networks (ANNs)-based backcalculation models were employed to rapidly and accurately predict flexible airport pavement layer moduli from realistic FWD deflection basins acquired at the U.S. Federal Aviation Administration's National Airport Pavement Test Facility (NAPTF). The uniformity characteristics of NAPTF flexible pavements were successfully mapped using the ANN predictions.     

A Spatial‐Bayesian Technique for Imputing Pavement Network Repair Data

Abstract: Pavement construction and repair history is necessary for several pavement management functions such as developing pavement condition prediction models and developing maintenance and rehabilitation (M&R) trigger values based on past repair frequencies. It is often difficult to integrate M&R data with condition data since these data are often stored in disparate heterogeneous databases. This article provides a computational technique for estimating construction and M&R history of a pavement network from the spatiotemporal patterns of its condition data. The technique is founded on Bayesian and spatial statistics and searches pavement condition data in groups of adjacent pavement sections for evidence of repair. The developed technique was applied to a pavement network in Texas and has been found to have a 74% precision and a 95% accuracy in estimating repair history data.     

FWD检测技术在路面养护工程中的应用

锤式弯沉仪(简称FWD)作为无损检测设备已经被广泛地应用于路面结构层评价,但缺乏针对破损路面检测的成果和工具。以嘉浏高速公路养护工程为依托,开发了针对高速公路破损路面结构层评价的程序,并探索FWD在实际工程中的应用。     

Fuzzy Representation of Pavement Condition for Efficient Pavement Management

Abstract: Many government agencies and private consulting companies manage large pavement networks in terms of infrastructure condition assessment and maintenance planning. Efficient pavement management is supported by pavement management systems (PMSs), which includes models for pavement condition assessments considered “valuable” by agency's engineers. The objective of this article is to define a pavement condition model able to overcome surveyors’ subjectivity in rating distresses and thus provide meaningful pavement conditions for the agencies to employ in project planning. The article proposes a fuzzy inference model for calculating pavement condition ratio (PCR) specifically tailored on the Alabama Department of Transportation Pavement (ALDOT) guidelines and policies. Applied to several surveyors’ ratings, the proposed model has the ability to smooth distress extent differences among surveyors producing PCR values within acceptable range of variability. The proposed approach has the intention of not only enhancing pavement condition characterization but also to exploit the opportunity made available by automation in the collection and interpretation of pavement data which are anyway characterized by an inherent subjectivity.     

落锤式弯沉仪(FWD)与贝克曼梁(BB)的相关性分析

以古神公路沥青混凝土路面为研究对象,分析了贝克曼梁与落锤式弯沉仪(FWD)在检测沥青混凝土路面弯沉时的相关性,并建立FWD与贝克曼梁检测数据的线性回归公式,对该地区道路状况评定具有借鉴意义。     

沥青路面动应变响应及其动荷载作用等效换算

通过现场动态实测试验,获取了在FWD冲击荷载与BZZ-100标准行车荷载作用下沥青面层底部的应变响应特征及其温度敏感性,研究了FWD冲击荷载与标准行车荷载作用间的等效换算关系。研究表明:在动荷载作用下,沥青面层底部主要呈受拉状态,其纵向应变响应大于横向应变;应变响应量随FWD荷载增大而增大,随行车荷载车速增大而减小;面层层底应变响应的温度敏感性主要与路面结构组合有关,受荷载变化的影响较小;面层底应变响应的温度敏感性排序为:倒装式路面S2>组合式路面S3>半刚性路面S1;随着FWD荷载增大,与其等效的行车荷载所对应的车速水平逐渐降低;在相同FWD荷载下,不同类型路面对应的等效行车荷载速度水平为:S1>S3>S2;建立了FWD荷载与标准行车荷载间的等效换算关系,有助于FWD荷载更准确地模拟实际行车荷载。     

Airfield pavement deterioration assessment using stress-dependent neural network models

In this study, an artificial neural network (ANN)-based approach was employed to backcalculate the asphalt concrete and non-linear stress-dependent subgrade moduli from non-destructive test (NDT) data acquired at the Federal Aviation Administration's National Airport Pavement Test Facility (NAPTF) during full-scale traffic testing. The ANN models were trained with results from an axisymmetric finite element pavement structural model. Using the ANN-predicted moduli based on the NDT test results, the relative severity effects of simulated Boeing 777 (B777) and Boeing 747 (B747) aircraft gear trafficking on the structural deterioration of NAPTF flexible pavement test sections were characterized. The results indicate the potential of using lower force amplitude NDT test data for routine airport pavement structural evaluation, as long as they generate sufficient deflections for reliable data acquisition. Therefore, NDT tests that employ force amplitudes at prototypical aircraft loading may not be necessary to evaluate airport pavements.     

Toward an Integrated Smart Sensing System and Data Interpretation Techniques for Pavement Fatigue Monitoring

Abstract: Currently, pavement instrumentation for condition monitoring is done on a localized and short‐term basis. Existing technology does not allow for continuous long‐term monitoring and network level deployment. Long‐term monitoring of mechanical loading for pavement structures could reduce maintenance costs, improve longevity, and enhance safety. In this article, on‐going research to develop and validate a smart pavement monitoring system is described. The system mainly consists of a novel self‐powered wireless sensor based on the integration of piezoelectric transduction with floating‐gate injection capable of detecting, storing, and transmitting strain history for long‐term monitoring and a novel passive temperature gauge. A technique for estimating full‐field strain distributions using measured data from a limited number of implemented sensors is also described. The ultimate purpose is to incorporate the traffic wander effect in the fatigue prediction algorithms. Preliminary results are shown and limitations are discussed.     

Probabilistic Neural Network for Reliability Assessment of Oil and Gas Pipelines

A fuzzy artificial neural network (ANN)–based approach is proposed for reliability assessment of oil and gas pipelines. The proposed ANN model is trained with field observation data collected using magnetic flux leakage (MFL) tools to characterize the actual condition of aging pipelines vulnerable to metal loss corrosion. The objective of this paper is to develop a simulation-based probabilistic neural network model to estimate the probability of failure of aging pipelines vulnerable to corrosion. The approach is to transform a simulation-based probabilistic analysis framework to estimate the pipeline reliability into an adaptable connectionist representation, using supervised training to initialize the weights so that the adaptable neural network predicts the probability of failure for oil and gas pipelines. This ANN model uses eight pipe parameters as input variables. The output variable is the probability of failure. The proposed method is generic, and it can be applied to several decision problems related with the maintenance of aging engineering systems.     

Effects of cross-anisotropy and stress-dependency of pavement layers on pavement responses under dynamic truck loading

Previous studies by the authors have determined pavement responses under dynamic loading considering cross-anisotropy in one layer only,either the cross-anisotropic viscoelastic asphalt concrete(AC)layer or the cross-anisotropic stress-dependent base layer,but not both.This study evaluates pavement stressestrain responses considering cross-anisotropy in all layers,i.e.AC,base and subbase,using finite element modeling(FEM) technique.An instrumented pavement section on Interstate I-40 near Albuquerque,New Mexico was used in ABAQUS framework as model geometry.Field asphalt cores were collected and tested in the laboratory to determine the cross-anisotropy(n-values) defined by horizontal to vertical modulus ratio,and other viscoelastic parameters as inputs of the model incorporated through user defined material interface(UMAT) functionality in ABAQUS.Field base and subbase materials were also collected and tested in the laboratory to determine stress-dependent nonlinear elastic model parameters,as inputs of the model,again incorporated through UMAT.The model validation task was carried out using field-measured deflections and strain values under falling weight deflectometer(FWD)loads at the instrumented section.The validated model was then subjected to an actual truck loading for studying cross-anisotropic effects.It was observed that horizontal tensile strain at the bottom of the AC layer and vertical strains in all layers decreased with an increase in n-value of the asphalt layer,from n1(anisotropy) to n=1(isotropy).This indicates that the increase in horizontal modulus caused the decrease in layer strains.It was also observed that if the base and subbase layers were considered stressdependent instead of linear elastic unbound layers,the horizontal tensile strain at the bottom of the asphalt layer increased and vertical strains on top of the base and subbase also increased.     

Assessing artificial neural network performance for predicting interlayer conditions and layer modulus of multi-layered flexible pavement

The objective of this study is to evaluate the performance of the artificial neural network (ANN) approach for predicting interlayer conditions and layer modulus of a multi-layered flexible pavement structure. To achieve this goal, two ANN based back-calculation models were proposed to predict the interlayer conditions and layer modulus of the pavement structure. The corresponding database built with ANSYS based finite element method computations for four types of a structure subjected to falling weight deflectometer load. In addition, two proposed ANN models were verified by comparing the results of ANN models with the results of PADAL and double multiple regression models. The measured pavement deflection basin data was used for the verifications. The comparing results concluded that there are no significant differences between the results estimated by ANN and double multiple regression models. PADAL modeling results were not accurate due to the inability to reflect the real pavement structure because pavement structure was not completely continuous. The prediction and verification results concluded that the proposed back-calculation model developed with ANN could be used to accurately predict layer modulus and interlayer conditions. In addition, the back-calculation model avoided the back-calculation errors by considering the interlayer condition, which was barely considered by former models reported in the published studies.     

沥青路面反算模量与沥青混合料动态模量的关系

为获取沥青路面结构层沥青材料模量参数,合理评价沥青路面结构性能,开展沥青路面反算模量与同温度下室内沥青混合料动态模量的关系研究。采用落锤式弯沉仪（FWD）对4个不同结构的沥青路面试验路进行测试,并通过路面结构埋设的温度传感器同步采集温度,对试验结果进行沥青层模量反算;采用沥青混合料性能试验机（AMPT）对试验路沥青材料进行动态模量试验,根据时温等效原理获取FWD测试的同温度下的沥青混合料模量值,结合沥青路面结构层厚度计算沥青混合料动态模量的当量模量;对沥青层同一温度下的FWD反算模量与动态模量的当量模量进行分析比较,建立回归模型。结果表明,不同路面结构的FWD反算模量与室内动态模量的关系基本一致,其变化趋势不依赖于沥青层厚度的变化;沥青路面FWD的反算模量和室内AMPT的模量呈非线性关系,当模量较小时,FWD反算模量要低于室内模量,随着模量的增加,在10000MPa附近时,二者的模量值是接近的,模量值再继续增大时,FWD反算模量的增加较快,明显大于室内动态模量,室内动态模量的增长趋于平缓。     

Pavement performance assessment using a cost‐effective wireless accelerometer system

Pavement condition monitoring is required to identify pavements in need of maintenance or rehabilitation. Early identification of reduction in pavement's structural resistance and improving the structural resistance by minor repairs can lead to significantly lower maintenance costs for transportation agencies. In this study, a cost‐effective wireless sensor that can be embedded in the road to measure the transient vibrations due to different applied loads was tested to determine its effectiveness in terms of pavement displacement measurements. Test results show that the vibration sensor, combined with the algorithms, can be embedded in new or existing pavements and used as an accurate wireless displacement sensor. The low cost of the sensor system allows the use of these sensors at high densities for monitoring the performance of an entire road network. Outputs from the developed system can be directly used to evaluate the condition and performance of pavement structure (increasing displacement over time indicating increasing pavement damage). In addition, displacement data from the system can be used to backcalculate pavement layer stiffnesses, which can be used to predict long‐term performance of the pavement structure. Reduction in pavement layer stiffness over time can be used to determine long‐term damage accumulation.     

Prediction of Pavement Performance through Neuro-Fuzzy Reasoning

Abstract:  Government agencies and consulting companies in charge of pavement management face the challenge of maintaining pavements in serviceable conditions throughout their life from the functional and structural standpoints. For this, the assessment and prediction of the pavement conditions are crucial. This study proposes a neuro-fuzzy model to predict the performance of flexible pavements using the parameters routinely collected by agencies to characterize the condition of an existing pavement. These parameters are generally obtained by performing falling weight deflectometer tests and monitoring the development of distresses on the pavement surface. The proposed hybrid model for predicting pavement performance was characterized by multilayer, feedforward neural networks that led the reasoning process of the IF-THEN fuzzy rules. The results of the neuro-fuzzy model were superior to those of the linear regression model in terms of accuracy in the approximation. The proposed neuro-fuzzy model showed good generalization capability, and the evaluation of the model performance produced satisfactory results, demonstrating the efficiency and potential of these new mathematical modeling techniques .     

A Pavement Condition-Rating Model Using Backpropagation Neural Networks

Abstract: This paper presents an overview of the neural-network technique as a management tool for maintenance of flexible pavement. The paper discusses the development and implementation of a neural network for the condition rating of roadway sections. The condition-rating scheme developed by Oregon State Department of Transportation was used as the basis for the development of the network presented. A training set of 744 cases was used to train the network, and a set of 1736 cases was used to test the generalization ability of the system. The network adequately learned the training examples with an average training error of 0.019 and was able to determine the correct condition ratings with an average testing error of 0.023. The network's ability to deal with noisy data also was tested. Up to 60% noise was added to the data and introduced to the network. The results showed that the network presented could identify condition rating relationships at high levels of-noise. Finally, an expert determination was compared with that produced by the network. The network was able to mimic the expert's condition ratings with an average error of 0.0354.     

谈路面维护和修复管理系统的离散优化模型

随着公路建设和高速公路网在的迅速发展,路面维护和修复活动的管理已变得尤为重要。论文提出了涉及管理体制4个不同群体的离散优化模型,不同群体分别为:政府、高速公路代理人、承包商和普通用户。这4个最优决策模型的制定及二元决策变量的线性整数规划问题。目标函数和约束条件都是基于路面状况指数。数值实验对四川省的公路系统的数据进行分析,其表明了该模型的可行性和有效性。     

UNCERTAINTIES OF ASPHALT LAYER THICKNESS DETERMINATION IN FLEXIBLE PAVEMENTS-INFLUENCE DIAGRAM APPROACH

The thickness of pavement layers is an important parameter used in Pavement Management Systems (PMS). Thickness data are used for pavement condition assessment, performance predictions, selection of maintenance strategies and rehabilitation treatments, basic quality assessment, and as input to overlay thickness design. Pavement thickness is usually determined from direct testing such core samples, nondestructive testing such as radar, or historical records such as pavements network database. This paper proposes the use of Bayesian Influence Diagrams as a tool in providing a probabilistic model for thickness determination procedure in flexible pavements. The Bayesian Influence Diagram Model is presented as a framework for addressing uncertainties involved in capturing quantitative and qualitative information in the asphalt layer thickness determination procedures. The model is also used to perform value of information analysis in the determination of pavement layer thickness. The Influence Diagram representation facilitates the assessment of coherent prior distributions and makes it easier for knowledge engineers and other decision makers to express and understand more general kinds of dependency and independency assumptions.     

基于模态应变能与神经网络的钢网架损伤检测方法

神经网络通过对样本的学习,获得结构模态参数与损伤之间的映射关系。目前基于神经网络的损伤检测已经越来越广泛地使用在非破坏性损伤诊断当中。但对于大型结构而言,它的训练样本数量过大,将消耗大量的计算。所以如何降低神经网络的计算量使其可用于大型结构的损伤诊断是一个亟待解决的问题。为了解决这个问题,提出了空间钢网架损伤的两步诊断法:第一步,利用模态应变能对结构损伤的敏感性,判断出结构损伤的可能位置;第二步,利用神经网络从可能发生损伤的杆件中定位出实际损伤的位置,并进行损伤程度的判断。利用一个空间网架作为数值算例,进行可行性验证。结果表明此方法可以准确判断出结构的损伤位置以及损伤大小,是一种行之有效的方法。     

Instantaneous pavement condition evaluation using non-destructive neuro-evolutionary approach

In this paper, a hybrid neural network (NN)-genetic algorithm (GA) based non-destructive pavement auscultation method for instantaneous airfield infrastructure condition assessment is discussed. NNs are employed for finite element aided forward prediction of pavement surface deflections resulting from non-destructive test impulse loading and the GAs are used for global optimisation of the pavement structural parameters by matching the NN predicted deflections with the measured pavement response. This hybrid approach takes advantage of the non-linear estimation capability provided by neural networks trained using finite element (FE) solutions in modelling the stress-dependent behaviour of unbound pavement geo-materials while improving the robustness to measurement uncertainty through the application of genetic algorithms. The performance of the developed hybrid pavement auscultation technique is evaluated through extensive field studies conducted at a state-of-the-art full-scale airfield pavement test facility. The results show that this approach is promising for real-time condition evaluation of airfield pavement infrastructure systems.     

A Comparative Analysis of Two Artificial Neural Networks Using Pavement Performance Prediction

The management of pavements requires the ongoing allocation of substantial manpower and capital resources by the responsible agencies. These agencies ultimately report to the executive and legislative branches of government, which require justification and proof of the efficacy of these expenditures. This and the need for improved engineering technical feedback have encouraged the development of pavement management systems (PMS). One goal of a PMS is to provide decision makers at all levels with optimal resource-allocation strategies. This requires evaluation of alternatives over an analysis period based on predicted values of pavement performance. This necessitates more reliable pavement performance prediction models. Traditional modeling uses multiple regression techniques to predict pavement performance from traffic, time, and pavement distress or various combinations of these factors. Within the last 10 years, new modeling techniques, including artificial neural networks (ANNs), have been applied to transportation problems. The ANNs examined usually have been of a single type called a dot product ANN. This paper examines a different type called the quadratic function ANN and compares the results to the dot product ANN. The quadratic function ANN is a generalized adaptive, feedforward neural network that combines supervised and self-organizing learning. Models were developed to predict roughness using both types of ANN on the same data samples and the results compared. The data samples were drawn from the Kansas Department of Transportation's PMS database. The results indicate a significant improvement in the use of the self-organizing quadratic function ANNs and lead to recommendations for specific areas of additional research.