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.

As urban construction continues to develop and automobile ownership rises, parking shortages in cities have become increasingly acute. Given the limited availability of land resources, conventional underground garages and parking buildings no longer suffice to meet the growing demand for parking spaces. To address this dilemma, underground parking shaft (UPS) has emerged as a highly regarded solution. This study provides an overview of the layout scheme, structural design approaches, and construction techniques for UPS, focusing on the characteristics of intensive construction demonstrated in the project located in the Jianye District of Nanjing. Compared to conventional vertical shaft garage construction methods, this assembly parking shaft offers advantages such as a smaller footprint, higher prefabrication rate, shorter construction period, and reduced environmental impact. It presents an efficient approach for the intensive construction of urban underground spaces, particularly in areas with limited land and complex environments, showing promising prospects for widespread application.

This paper presents a calculation method that evaluates the extent of disturbance based on structural safety limits. Additionally, it summarizes the assessment methods for construction disturbance zones in shield tunneling near pile foundations, urban ground structures, and underground structures. Furthermore, taking the construction of the Chengdu Jinxiu Tunnel under bridges and urban pipelines as the engineering background, a study on the disturbance zoning of adjacent structures was conducted. The most intense disturbance occurs within one week of the tunnel underpass process, and it has a significant impact within a range of two times the tunnel diameter along the tunnel axis. The bridge pile and bridge deck experience less disturbance from tunnel approaching construction, with a maximum disturbance zone characterized as medium disturbance. On the other hand, underground pipelines are subjected to more significant disturbances from tunnel construction, with a maximum disturbance zone classified as strong disturbance. The implementation of “bridge pile sleeve valve pipe grouting & underground pipeline ground grouting & tunnel advance grouting” in the field effectively limits the vertical settlement of bridges and pipelines, resulting in a decrease of approximately 0.1 in disturbance level for the structures. The disturbance zoning method can assess tunnel disturbance with structures, identify high-risk interference locations, and facilitate targeted design reinforcement solutions.

Though a comprehensive in situ measurement project, the performance of a deep pit-in-pit excavation constructed by the top-down method in seasonal frozen soil area in Shenyang was extensively examined. The measured excavation responses included the displacement of capping beam and retaining pile, settlement of ground surface, and deformation of metro lines. Based on the analyses of field data, some major findings were obtained: 1) the deformations of retaining structures fluctuated along with the increase of temperature, 2) the deformation variation of retaining structures after the occurrence of thawing of seasonal frozen soil was greater than that in winter, although the excavation depth was smaller than before, 3) the influence area of ground settlement was much smaller because of the features of seasonal frozen sandy soil, 4) the displacement of metro line showed a significant spatial effect, and the tunnel lining had an obviously hogging displacement pattern, and 5) earth pressure redistribution occurred due to the combined effects of freezing-thawing of seasonal frozen soil and excavation, leading to the deformation of metro line. The influence area of ground settlement was obviously smaller than that of Shanghai soft clay or other cases reported in literatures because of special geological conditions of Shenyang. However, the deformation of metro lines was significantly lager after the thawing of the frozen soil, the stress in deep soil was redistributed, and the metro lines were forced to deform to meet a new state of equilibrium.

Intelligent construction has become an inevitable trend in the development of the construction industry. In the excavation project, using machine learning methods for early warning can improve construction efficiency and quality and reduce the chances of damage in the excavation process. An interpretable gradient boosting based ensemble learning framework enhanced by the African Vultures Optimization Algorithm (AVOA) was proposed and evaluated in estimating the diaphragm wall deflections induced by excavation. We investigated and compared the performance of machine learning models in predicting deflections induced by excavation based on a database generated by finite element simulations. First, we exploratively analyzed these data to discover the relationship between features. We used several state-of-the-art intelligent models based on gradient boosting and several simple models for model selection. The hyperparameters for all models in evaluation are optimized using AVOA, and then the optimized models are assembled into a unified framework for fairness assessment. The comprehensive evaluation results show that the AVOA-CatBoost built in this paper performs well (RMSE = 1.84, MAE = 1.18, R² = 0.9993) and cross-validation (RMSE = 2.65 ± 1.54, MAE = 1.17 ± 0.23, R² = 0.998 ± 0.002). In the end, in order to improve the transparency and usefulness of the model, we constructed an interpretable model from both global and local perspectives.

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.

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.

Collecting and analyzing vibration signals from structures under time-varying excitations is a non-destructive structural health monitoring approach that can provide meaningful information about the structures’ safety without interrupting their normal operations. This paper develops a novel framework using prompt engineering for seamlessly integrating users’ domain knowledge about vibration signals with the advanced inference ability of well-trained large language models (LLMs) to accurately identify the actual states of structures. The proposed framework involves formulating collected data into a standardized form, utilizing various prompts to gain useful insights into the dynamic characteristics of vibration signals, and implementing an in-house program with the help of LLMs to perform damage detection. The advantages, as well as limitations, of the proposed method are qualitatively and quantitatively assessed through two realistic case studies from literature, demonstrating that the present method is a new way to quickly construct practical and reliable structural health monitoring applications without requiring advanced programming/mathematical skills or obscure specialized programs.

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.

The study aims to develop machine learning-based mechanisms that can accurately predict the axial capacity of high-strength concrete-filled steel tube (CFST) columns. Precisely predicting the axial capacity of a CFST column is always challenging for engineers. Using artificial neural networks (ANNs), random forest (RF), and extreme gradient boosting (XG-Boost), a total of 165 experimental data sets were analyzed. The selected input parameters included the steel tensile strength, concrete compressive strength, tube diameter, tube thickness, and column length. The results indicated that the ANN and RF demonstrated a coefficient of determination (R²) value of 0.965 and 0.952 during the training and 0.923 and 0.793 during the testing phase. The most effective technique was the XG-Boost due to its high efficiency, optimizing the gradient boosting, capturing complex patterns, and incorporating regularization to prevent overfitting. The outstanding R² values of 0.991 and 0.946 during the training and testing were achieved. Due to flexibility in model hyperparameter tuning and customization options, the XG-Boost model demonstrated the lowest values of root mean square error and mean absolute error compared to the other methods. According to the findings, the diameter of CFST columns has the greatest impact on the output, while the column length has the least influence on the ultimate bearing capacity.