Graded crushed stone (GCS), as a cheap and essential component, is of great importance in road construction. The irregularity and variability of particle shape is known to affect the packing characteristics of GCS, such as compactness and void ratio. In this study, the realistic particle outline is first automatically extracted based on digital image processing and deep learning algorithms. Then, the elongation (EI), roundness (Rd), and roughness (Rg) of GCS are quantified by shape evaluation algorithms. Moreover, based on the establishment of the GCS shape library, the gravity deposition with various elongations is simulated using the discrete element method to study the packing features of GCS. The elongation effects on the macroscopic and microscopic quantities are explored. Finally, the shear behavior of GCS is studied. The results illustrate that elongation has a significant effect on the packing of GCS.

In this study, a depth camera-based intelligence method is proposed. First, road damage images are collected and transformed into a training set. Then training, defect detection, defect extraction, and classification are performed. In addition, a YOLOv5 is used to create, train, validate, and test the label database. The method does not require a predetermined distance between the measurement target and the sensor; can be applied to moving scenes; and is important for the detection, classification, and quantification of pavement diseases. The results show that the sensor can achieve plane fitting at investigated working distances by means of a deep learning network. In addition, two pavement examples show that the detection method can save a lot of manpower and improve the detection efficiency with certain accuracy.

Subway shield lining segments are highly sensitive to peripheral soil disturbance. Shield displacement may destroy the whole structure which can not be repaired, and directly affects the subway operation safety at the worst. Therefore, it is necessary to reasonably determine the construction technology, in order to avoid the unfavorable influences caused by construction disturbance. According to the pile-board subgrade scheme which is adopted by the comparison of schemes, also considering the high risk of over-shallow-embedded metro construction, we put forward the key construction technique of pile-board subgrade, including: technique of casing tube bored pile, construction process of pile-board structure and monitoring of settlement and deformation which are important to guide the similar projects.

It’s the first application of NEPBS crossing over-shallow-embedded metro in deep soft ground on new Shanghai-Hangzhou passenger dedicated railway (SHPDR) in China. It’s necessary to study the engineering behavior of NEPBS in complex geology field. Long-term observation systems were installed at the typical test sections of SHPDR; Combined with theoretical analysis model, the mechanical properties and deformation laws were analyzed and tested. Measurement results show that the test reinforcement stresses of loading plates and supporting beams are small; the test bending moments agree well with the theoretical bending moments, which are far less than the resistance bending moments; the piles improved the mechanical properties of deep soft ground, so the plate-soil contact stresses are small, the maximum value is 132 kPa; 45 days after the train operation, the post-construction settlement is 1.86 mm; the settlements of individual test points are not yet stable, but they tend toward stability, which can meet the requirements of settlement control of SHPDR.

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