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This study aims to develop a new in situ nondestructive testing (NDT) method to determine nonlinear parameters of subgrade soils and to establish a method to calculate the subgrade design modulus. The new method collects nonlinear characteristics of the subgrade by in situ test with various combinations of dynamic stress and static stress and back-calculates subgrade nonlinear parameters by machine learning algorithm. The highlight of this study is to solve the problem that the results of different test methods are not unique, and the stress-dependence characteristics of the subgrade are fully considered. The main work is as follows: (1) develop a new resilient modulus prediction model considering dynamic and static effects; (2) propose a novel in situ NDT method based on loading sequences; (3) establish a fuzzy back-propagation neural network (BPNN) algorithm to compute subgrade nonlinear parameters; and (4) propose a calculation method of subgrade design modulus based on the principle of deflection equivalence and establish an empirical equation of subgrade design modulus. The results showed that the contact stress has an enhancing effect on resilient modulus of subgrade soils. The new in situ NDT method has been proved feasible in theory and can accurately obtain the nonlinear characteristics of the subgrade. [ABSTRACT FROM AUTHOR]

The deformation behaviour of the asphalt concrete (AC) depends on the loading state and the temperature, accompanied by a large number of microcracks. In this study, the deformation and failure behaviour of the AC under uniaxial compression were investigated by the acoustic emission (AE). Both the stress–strain relationship and the energy dissipation were employed to analyse the macroscopic deformation of the specimen at low and medium temperatures. The AE parameters including count, energy and amplitude were measured in real-time during the loading process. The damage morphology of specimens was characterised by the failure location with the AE technology. Results show that with the rise of the temperature, the strength of AC specimens gradually decreased, which leads to a lower capacity in storing strain energy. The dissipative energy is greatly affected by the damage degree of the specimen. Both the event count and amplitude increased rapidly when the specimen failure occurred. The AE parameters are relatively sensitive to the deformation failure of asphalt mixtures influenced by the temperature. Furthermore, the results also imply that the AE method used in the present study to characterise the deformation failure in pavement engineering is more scientific and precise. [ABSTRACT FROM AUTHOR]

Driven by the huge thermal energy in cement concrete pavements, thermoelectric (TE) cement has attracted considerable attention. However, the current TE cement shows poor performance, which greatly limits its application. Herein, a series of Bi0.5Sb1.5Te3/carbon nanotubes (CNTs) co-reinforced cement composites have been prepared, and their TE properties were systematically investigated. It was shown that the addition of Bi0.5Sb1.5Te3 particles can effectively improve the TE properties of CNTs-reinforced cement composites by building a better conductive network, increasing energy filtering and interfaces scattering. The Bi0.5Sb1.5Te3/CNTs cement composites with 0.6 vol.% of Bi0.5Sb1.5Te3 exhibits the highest ZT value of 1.2 × 10−2, increased by 842 times compared to that of the CNTs-reinforced cement composites without Bi0.5Sb1.5Te3. The power output of this sample with the size of 2.5 × 3.5 × 12 mm3 reaches 0.002 μW at a temperature difference of 19.1 K. These findings shed new light on the development of high-performance TE cement, which can guide continued advances in their potential application of harvesting thermal energy from pavements. [ABSTRACT FROM AUTHOR]