防治水泥稳定碎石基层沥青路面裂缝的措施浅析

水泥稳定碎石基层沥青路面裂缝产生的原因复杂 ,但非荷载型裂缝更为主要。防治水泥稳定碎石基层沥青路面的裂缝应该从设计和施工两方面考虑 ,其中由结构、材料组成设计 ,以及施工工艺出发控制水泥稳定碎石基层本身的收缩也是一项重要措施。     

Pavement crack detection using hessian structure propagation

Pavement images are widely used in transportation agencies to detect cracks accurately so that the best proper plans of maintenance and rehabilitation could be made. Although crack in a pavement image is perceived because the intensity of crack pixels contrasts with that of the pavement background, there are still challenges in distinguishing cracks from complex textures, heavy noise, and interference. Unlike the intensity or the first-order edge feature of crack, this paper proposes the second-order directional derivative to characterize the directional valley-like structure of crack. The multi-scale Hessian structure is first proposed to analytically adapt to the direction and valley of cracking in the Gaussian scale space. The crack structure field is then proposed to mimic the curvilinear propagation of crack in the local area, which is iteratively applied at every point of the crack curve to infer the crack structure at the gaps and intersections. Finally, the most salient centerline of the crack within its curvilinear buffer is exactly located with non-maximum suppression along the perpendicular direction of crack. The experiments on large numbers of images of various crack types and with diverse conditions of noise, illumination and interference demonstrate the proposed method can detect pavement cracks well with an average Precision, Recall and F-measure of 92.4%, 88.4%, and 90.4% respectively. Also, the proposed method achieves the best performance of crack detection on the benchmark datasets among methods that also require no training and publicly offer the detection results for every image.     

基于自适应灰色模型的路面使用性能预测

在灰色理论建模的基础上,通过采用预测的最新数据替代最老数据这种方式,建立了自适应灰色模型,并将其应用于路面使用性能预测。能够克服缺少历史数据的不足,同时,与以往的预测模型相比,更加准确,更加符合路面使用性能的变化规律。通过MATLAB编程实现,使得预测更加简便易行。     

路面激扰与电磁激扰下电动轮车辆系统非线性全局动态特性研究

为研究电动轮车辆系统在路面 电磁联合激励下的非线性振动特性,并挖掘参数与初始条件协同作用下系统全局动力学信息,建立了计及悬架系统和轮胎的非线性弹簧力和阻尼力的电动轮车辆系统两自由度1/4垂向振动非光滑非线性时变动力学模型,考虑外部激励的谐波性和随机性以及电磁激励的分段周期性,得到了含谐波性、随机性和周期性的复杂外激励模型,分析了驱动电机转速与多初值的关联性对系统动力学共存行为及其分岔的影响规律,研究了共存运动的多样性及其吸引域的拓扑构型,揭示了转速对系统全局动力学稳定性的影响机理.结果表明：电机转速与多初值协同作用诱发系统出现多样性的共存运动,共存运动吸引域的拓扑结构复杂多样,部分共存运动的振动幅值可能较大亦可能很小,其全局动力学稳定性差异较大.电机转速与初值的协同作用对电动轮车辆系统非线性振动和全局动态特性的影响不予忽视.本文研究对电动轮车辆系统的动态性能改善和结构优化具有重要的理论意义和工程价值.     

基于线结构光传感器的公路平整度测量系统

研制了基于线结构光传感器的公路平整度测量系统,用于测量铺设完成但未凝固之前的水泥路面平整度。该系统利用四套线结构光传感器来测量路面特定点的三维数据,通过激光测距仪获得距离信息,提出了一种测量系统运动颠簸误差修正方法,设计并研制了颠簸误差传感器。设计了一种新型全局平面靶标,可一步同时完成多个传感器摄像机内、外部参数和光平面位置参数的快速标定与坐标系统一。对系统进行了标定实验、精度验证实验及实际测量实验,实验结果证明该系统测量精度可达到0.2mm以上。     

Optimisation of maintenance strategy for rural road network using genetic algorithm

The objective of this paper is to develop an optimised maintenance strategy for the rural road network of Kerala state. This is accomplished with the development of a bi-objective deterministic optimisation model which simultaneously satisfies the objectives of both minimisation of total maintenance cost and maximisation of performance of the road network. The model is capable of planning the maintenance activities over a multi-year planning period. The performance of the road network is accounted using the composite index, namely Pavement Condition Index. The constraint-based genetic algorithm was used as the optimisation tool since it very well takes care of the combinatorial nature of the network-level pavement maintenance programming. The applicability of the model is illustrated using a case study for the rural road network of Kerala state in India. The effort made through this research work to develop a suitable Pavement Maintenance and Management System for rural road network can lead to the proper maintenance and upkeep of the rural roads, thereby triggering a positive impact on the Indian economy.     

Multi-scale wavelet transform filtering of non-uniform pavement surface image background for automated pavement distress identification

Non-uniform background of pavement images results in difficulties when segmenting pavement images for pavement distress identification. A novel and fast non-uniform background removal algorithm based on multi-scale wavelet transform is presented. The algorithm uses multi-scale wavelet transform to decompose pavement image and then reconstructs image background using low-wavenumber components through inverse wavelet transform. Brightness of image background is then corrected to achieve uniform background pavement image. The multi-scale wavelet transform algorithm is compared with median filter algorithm and morphological closing algorithm. Experimental results show that the proposed algorithm possesses the advantage of extracting tiny cracks more effectively than the other two algorithms, demonstrating its suitability to be used in automated pavement distress segmentation and identification.     

Evaluation of back-calculated elastic moduli of unreinforced and geocell-reinforced unbound granular material from full-scale field tests

This paper presents a field-scale experimental track over a poor subgrade with an unreinforced section and a geocell-reinforced section subjected to in-situ performance tests. Plate load tests and Benkelman beam tests were carried out distributed in several unreinforced and reinforced layers. The objective was to: (1) examine the variability of the elastic modulus of unbound granular material (UGM) due the influence of its thickness and the presence of poor subgrade in its base, (2) evaluate the modulus improvement factor (MIF) generated by the geocell reinforcement in the UGM and (3) verify the most appropriate condition to apply the MIF to transport infrastructure design. The results showed that there is a significant influence of the thickness of the UGM layer on its elastic modulus when the layer is supported directly over a soft subgrade. The MIF values obtained in field suggest that its determination is mostly related to the UGM maximum elastic modulus rather than its decreased values (by virtue of poor subgrade or reduced thicknesses), and that the analytical formulation presented for MIF calculation has good predictive capability to be applied to pavement design.     

Prediction of the crack condition of highway pavements using machine learning models

Departments of Transportation regularly evaluate the condition of pavements through visual inspections, nondestructive evaluations, image recognition models and learning algorithms. The above methodologies, though efficient, have drawn attention due to their subjective errors, uncertainties, noise effects and overfitting. To improve on the outcomes of the shallow learning models already used in pavement crack prediction, this paper reports on an investigation of the use of recursive partitioning and artificial neural networks (ANN; deep learning frameworks) in predicting the crack rating of pavements. Explanatory variables such as the average daily traffic and truck factor, roadway functional class, asphalt thickness, and pavement condition time series data are employed in the model formulation. Overall, it is observed that the recursive partitioning (regression tree – R² > 0.8 and classification tree – R² > 0.6) and ANN (continuous response – R² > 0.8 and categorical response – R² > 0.6) are compelling machine learning models for the prediction of the crack ratings based on their goodness-of-fit statistics, mean absolute deviation (MAD < 0.4) and the root mean square errors (RMSE between 0.30 and 0.65).     

Reliability-based performance simulation for optimized pavement maintenance

Roadway pavement maintenance is essential for driver safety and highway infrastructure efficiency. However, regular preventive maintenance and rehabilitation (M&R) activities are extremely costly. Unfortunately, the funds available for the M&R of highway pavement are often given lower priority compared to other national development policies, therefore, available funds must be allocated wisely. Maintenance strategies are typically implemented by optimizing only the cost whilst the reliability of facility performance is neglected. This study proposes a novel algorithm using multi-objective particle swarm optimization (MOPSO) technique to evaluate the cost-reliability tradeoff in a flexible maintenance strategy based on non-dominant solutions. Moreover, a probabilistic model for regression parameters is employed to assess reliability-based performance. A numerical example of a highway pavement project is illustrated to demonstrate the efficacy of the proposed MOPSO algorithms. The analytical results show that the proposed approach can help decision makers to optimize roadway maintenance plans.