Review on the mesoscale characterization of cement-stabilized macadam materials

The base layer constructed by cement-stabilized macadam(CSM)has been widely used in highway construction due to its low elasticity deformation and high carrying capacity.As a bearing layer,the CSM base is not exempt from fatigue cracking under cyclic loading in the service process.Cracks in the base will create irreversible structural and functional deficiencies,such as the potential for reflective cracking of subsequently placed asphalt concrete overlays.The fracture of the base will shorten the service life of the pavement.The quality of the CSM base is directly related to the bearing capacity and integrity of the whole pavement structure.It is of practical significance to further study the fatigue failure behavior of CSM material for the long-term performance of the pavement.The CSM material is a typical heterogeneous multiphase composite.On the mesoscale,CSM consists of aggregate,cement mortar,pores,and the interface transitional zone(ITZ).On the microscale,the hardened mortar contains a large number of capillary pores,unhydrated particles,hydrated crystals,etc.,which makes the spatial distribution of its material properties stochastic.In addition,cement hydration,dry shrinkage,and temperature shrinkage can also produce micro-crack defects in cement mortar.These microcracks will have crossscale evolution under load,resulting in structural fracture.Macroscopic complex deformation and mechanical response are the reflections of its microscopic and even mesoscale composition and structure.This study summarized the existing studies on the mesoscopic properties of CSM materials,respectively from the three aspects of mesostructure,structural characterization,and mesoscale fatigue damage analysis,to help the development of long-life pavement.The future research direction is to explore the mesoscale characteristics of CSM using multiscale representation and analysis methods,to establish the connection between mesoscale characteristics and macroscopic mechanical properties.

Numerical simulation of mesoscopic cracking of cement-treated base material based on random polygon aggregate model

Cracking failure of cement-treated base(CTB)has always been the concern of highway constructors.Mesoscale cracking analysis is an important means to study the damage degradation mechanism,which is difficult to be characterized by experimental techniques alone.The objective of this paper is to develop a random aggregate modelling method to simulate the mesoscopic cracking of CTB material.A minimum rectangle area method was proposed to calculate the polygon aggregate size,which is closer to the sieving analysis than the average radius method.A buffer zone method was proposed to determine the distance between randomly generated polygon aggregates.Based on the proposed random algorithm,finite element method(FEM)was adopted to build the mesoscopic model of CTB including aggregate,mortar,interfacial transition zone(ITZ)and air voids.Laboratory tests were conducted to validate the numerical model.Then the sensitivity analyses were conducted to study the influencing factors on cracking behavior.The simulation results indicate that the higher aggregate content and the finer gradation lead to the increase of ITZ,thus reducing the cracking resistance of the CTB material.Low porosity content is able to significantly reduce the stress concentration and thus improves the cracking resistance.The research results of this paper could be used to guide the crack resistant design of CTB material.

Automatic sweet pepper detection based on point cloud images using subtractive clustering

Automatic identification and detection of fruit on trees by machine vision is the basis of developing automatic harvesting robots in agriculture.The occlusion of branches,leaves and other fruits in canopy images will affect the accuracy of fruit detection.To provide a scientific and reliable technical guidance for fruit harvesting robots,a method using point cloud images was proposed in this study to detect red fruits to overcome the impact of occlusion on detection.Firstly,the fruit regions were segmented from a tree’s point cloud by applying the color threshold of red and green.Then,the noise in fruit point clouds was removed with sparse outlier removal.Finally,the point cloud of each fruit was detected and counted based on the subtractive clustering algorithm.For the sweet pepper dataset,the true positive rate(TPR)is 90.69%and the false positive rate(FPR)is 6.97%for all fruits that are at least partially visible in the scene.