Using the complex variable method, an elastic analytical solution of the ground displacement caused by a shallow circular tunneling is derived. Non-symmetric deformation relative to the horizontal center line of the tunnel cross-section is used as a boundary condition. A comparison between the proposed analytical method and the Finite Element Method is carried out to validate the rationality of the obtained analytical solution. Two parameters in the Peck formula, namely the maximum settlement of the ground surface center and the width coefficient of settlement curve, are fitted and determined. We propose a modified Peck formula by considering three input parameters, namely the tunnel depth, tunnel radius, and the tunnel gap. The influence of these three parameters on the modified Peck formula is analyzed. The applicability of the modified Peck formula is further investigated by reference to six engineering projects. The ground surface displacement obtained by the explicit Peck formula is in good agreement with the field data, and the maximum error is only 1.3 cm. The proposed formula can quickly and reasonably predict the ground surface settlement caused by tunnelling.

Inverse problem-solving methods have found applications in various fields, such as structural mechanics, acoustics, and non-destructive testing. However, accurately solving inverse problems becomes challenging when observed data are incomplete. Fortunately, advancements in computer science have paved the way for data-based methods, enabling the discovery of nonlinear relationships within diverse data sets. In this paper, a step-by-step completion method of displacement information is introduced and a data-driven approach for predicting structural parameters is proposed. The accuracy of the proposed approach is 23.83% higher than that of the Genetic Algorithm, demonstrating the outstanding accuracy and efficiency of the data-driven approach. This work establishes a framework for solving mechanical inverse problems by leveraging a data-based method, and proposes a promising avenue for extending the application of the data-driven approach to structural health monitoring.

The conventional admittance approach utilizing statistical evaluation metrics offers limited information about the damage location, especially when damage introduces nonlinearities in admittance features. This study proposes a novel automated damage localization method for plate-like structures based on deep learning of raw admittance signals. A one-dimensional (1D) convolutional neural network (CNN)-based model is designed to automate processing of raw admittance response and prediction of damage probabilities across multiple locations in a monitored structure. Raw admittance data set is augmented with white noise to simulate realistic measurement conditions. Stratified K-fold cross-validation technique is employed for training and testing the network. The experimental validation of the proposed method shows that the proposed method can accurately identify the state and damage location in the plate with an average accuracy of 98%. Comparing with established 1D CNN models reveals superior performance of the proposed method, with significantly lower testing error. The proposed method exhibits the ability to directly handle raw electromechanical admittance responses and extract optimal features, overcoming limitations associated with traditional piezoelectric admittance approaches. By eliminating the need for signal preprocessing, this method holds promise for real-time damage monitoring of plate structures.

A highrise tower atop short columns in Nantong, China was threatened by excavation of a subway station nearby. Although an elaborate protection plan composed of isolation piles, artificial recharge and underpinning was executed throughout the excavations, the tower underwent unacceptable settlements and notable inclinations. In combination of field measurements and numerical simulations, this paper investigates the tower’s responses to the adjacent excavations, examines the effects of adopted protection plans and explores potential effective protection plans. First, the responses of the tower and the effectiveness of the three implemented measures were examined, and the contributory factors triggering intolerable tower deformations were identified; then, the effects of primary protection parameters were quantified, including the length, stiffness and layout of isolation piles, the water level surrounding recharge wells after recharging and the depth and location of wells, and the length of underpinning piles. It reveals that the underpinning plan had the best protection effect, followed by isolation piles and recharging wells. Construction timing of protection measures and termination manners of recharging are two critical factors in restraining tower deformations. Moreover, underpinning the tower with 36-m long steel pipe piles solely before implementation of adjacent excavations could be another optimal protection scheme.

While recycling is a topic of contemporary relevance, there is a scarcity of research on the engineering characteristics of construction and demolition wastes with different levels of grain strength and composition of debris, which impose constraints on their potential for reuse. This study aims to increase the use of recycled aggregates in fillings, addressing a gap in the literature. For this purpose, large-scale direct shear and California bearing ratio tests were conducted on nine diverse recycled aggregates from different construction works. The test outcomes were compared to those obtained from natural aggregates (NA) to draw a meaningful conclusion. The impact of the specimens’ water content and relative density on the findings was discussed. Results demonstrated that the shear strength of recycled aggregates is significantly affected by the compressive strength of the concrete within the recycled aggregates. Besides, increasing the percentage of NA or relative density improved the specimen’s shear strength. On the other hand, it was determined that the high water content of the crushed bricks reduced the fill’s quality. As a result of the study, equations were suggested for use in filling design.