Simulating pavement structural condition using artificial neural networks

This article aims to investigate the feasibility of incorporating of an artificial neural network (ANN) as an innovative technique for modelling the pavement structural condition, into pavement management systems. For the development of the ANN, strain assessment criteria are set in order to characterise the structural condition of flexible asphalt pavements with regards to fatigue failure. This initial task is directly followed with the development of an ANN model for the prediction of strains primarily based on in situ field gathered data and not through the usage of synthetic databases. For this purpose, falling weight deflectometer (FWD) measurements were systematically conducted on a highway network, with ground-penetrating radar providing the required pavement thickness data. The FWD data (i.e. deflections) were back-analysed in order to assess strains that would be utilised as output data in the process of developing the ANN model. A paper exercise demonstrates how the developed ANN model combined with the suggested conceptual approach for characterising pavement structural condition with regard to strain assessment could make provisions for pavement management activities, categorising network pavement sections according to the need for maintenance or rehabilitation. Preliminary results indicate that the ANN technique could help assist policy decision makers in deriving optimum strategies for the planning of pavement infrastructure maintenance.     

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。     

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.     

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。     

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.     

Study on the mesostructure and mesomechanical characteristics of the soil–rock mixture using digital image processing based finite element method

The soil–rock mixture (SRM) is a kind of inhomogeneous geomaterial, which poses difficulties of in situ sample acquisition and in laboratory geomaterial tests; hence, the study of the SRM's mechanical properties is still at an early stage. In this paper, the technique of digital image processing based on the finite element method (DIP-FEM) is introduced to study SRMs in the Leaping Tiger Gorge Reservoir Area, China. Based on the DIP, the mesostructural characteristics of the SRM are analyzed statistically. The mesostructural concept model of SRM that can actually represent the inhomogeneity of SRM is built. By using geometry vectorizaiton transformation, the mesostructural model of SRM in the binary image format has been translated into a vector format (such as DWG or DXF format) which can be imported into the finite element software. By using the finite element method, two large-scale direct shear tests of inhomogeneous SRM and homogeneous soil are simulated. The numerical results indicate that the existence of “rock” blocks in SRM will greatly influence the distribution and the failure models of the internal stress field. As a result, three kinds of failure models of the SRM are put forward.     

Structural Emergence: Architectural and Structural Design Collaboration at SOM

At Skidmore, Owings & Merrill (SOM) there is a strong culture of collaboration between architects and engineers, and innovation in computational technologies - the firm fervently embraced the computer over 50 years ago. Here a cross-disciplinary team from SOM, architects Keith Besserud and Neil Katz and structural engineer Alessandro Beghini , describe their current work on FE algorithms and how this has informed recent projects.     

Linking Structure and Parametric Geometry

The Karamba plug-in developed by Clemens Preisinger in collaboration with Bollinger + Grohmann Engineers has been developed to predict the behaviour of structures under external loads. Intended to be used by architects rather than being solely confined to an engineering setting, it enables a seamless flow of data between structural and geometric models. Preisinger here describes the program's evolution and application.