Real-time perception of rock conditions based on continuously collected data to meet the requirements of continuous Tunnel Boring Machine (TBM) construction presents a critical challenge that warrants increased attention. To achieve this goal, this paper establishes real-time prediction models for fractured and weak rock mass by comparing 6 different algorithms using real-time data collected by the TBM. The models are optimized in terms of selecting metric, selecting input features, and processing imbalanced data. The results demonstrate the following points. (1) The Youden’s index and area under the ROC curve (AUC) are the most appropriate performance metrics, and the XGBoost Random Forest (XGBRF) algorithm exhibits superior prediction and generalization performance. (2) The duration of the TBM loading phase is short, usually within a few minutes after the disc cutter contacts the tunnel face. A model based on the features during the loading phase has a miss rate of 21.8%, indicating that it can meet the early warning needs of TBM construction well. As the TBM continues to operate, the inclusion of features calculated from subsequent data collection can continuously correct the results of the real-time prediction model, ultimately reducing the miss rate to 16.1%. (3) Resampling the imbalanced data set can effectively improve the prediction by the model, while the XGBRF algorithm has certain advantages in dealing with the imbalanced data issue. When the model gives an alarm, the TBM operator and on-site engineer can be reminded and take some necessary measures for avoiding potential tunnel collapse. The real-time predication model can be a useful tool to increase the safety of TBM excavation.
For real-time classification of rock-masses in hard-rock tunnels, quick determination of the rock lithology on the tunnel face during construction is essential. Motivated by current breakthroughs in artificial intelligence technology in machine vision, a new automatic detection approach for classifying tunnel lithology based on tunnel face images was developed. The method benefits from residual learning for training a deep convolutional neural network (DCNN), and a multi-scale dilated convolutional attention block is proposed. The block with different dilation rates can provide various receptive fields, and thus it can extract multi-scale features. Moreover, the attention mechanism is utilized to select the salient features adaptively and further improve the performance of the model. In this study, an initial image data set made up of photographs of tunnel faces consisting of basalt, granite, siltstone, and tuff was first collected. After classifying and enhancing the training, validation, and testing data sets, a new image data set was generated. A comparison of the experimental findings demonstrated that the suggested approach outperforms previous classifiers in terms of various indicators, including accuracy, precision, recall, F1-score, and computing time. Finally, a visualization analysis was performed to explain the process of the network in the classification of tunnel lithology through feature extraction. Overall, this study demonstrates the potential of using artificial intelligence methods for in situ rock lithology classification utilizing geological images of the tunnel face.
Full-scale dome structures intrinsically have numerous sources of irreducible aleatoric uncertainties. A large-scale numerical simulation of the dome structure is required to quantify the effects of these sources on the dynamic performance of the structure using the finite element method (FEM). To reduce the heavy computational burden, a surrogate model of a dome structure was constructed to solve this problem. The dynamic global sensitivity of elastic and elastoplastic structures was analyzed in the uncertainty quantification framework using fully quantitative variance- and distribution-based methods through the surrogate model. The model considered the predominant sources of uncertainty that have a significant influence on the performance of the dome structure. The effects of the variables on the structural performance indicators were quantified using the sensitivity index values of the different performance states. Finally, the effects of the sample size and correlation function on the accuracy of the surrogate model as well as the effects of the surrogate accuracy and failure probability on the sensitivity index values are discussed. The results show that surrogate modeling has high computational efficiency and acceptable accuracy in the uncertainty quantification of large-scale structures subjected to earthquakes in comparison to the conventional FEM.
A concrete-filled double-skin tube (CFDST) is a new type of composite material. Experimental studies have been conducted to investigate the axial compression behavior of CFDST members for approximately 30 years. This paper provides a review of the status of axial compression bearing capacity tests conducted on circular CFDST stub columns as well as a summary of test data for 165 circular CFDST stub columns reported in 22 papers. A relatively complete high-quality test database is established. Based on this database, the main factors affecting the axial compression bearing capacity of the CFDST stub columns are analyzed. The prediction accuracy and robustness of an existing theoretical prediction model, which is a data-driven model, are evaluated, and a numerical simulation of the axial compression bearing capacity of the CFDST stub columns is conducted. In addition, the differences between the basic theory and experimental results of various models are compared, and the possible sources of prediction errors are analyzed. The current model for predicting the axial compression capacity of CFDST stub columns cannot simultaneously satisfy the requirements of high accuracy and confidence, and the stress independency assumption introduced in the test is not valid. The main error source in the theoretical prediction model is the non-simultaneous consideration of the effects of the void ratio and inner steel tube.
Previous research has shown that using buckling-restrained braces (BRBs) at hinged wall (HW) base (HWBB) can effectively mitigate lateral deformation of steel moment-resisting frames (MRFs) in earthquakes. Force-based and displacement-based design methods have been proposed to design HWBB to strengthen steel MRF and this paper comprehensively compares these two design methods, in terms of design steps, advantages/disadvantages, and structure responses. In addition, this paper investigates the building height below which the HW seismic moment demand can be properly controlled. First, 3-story, 9-story, and 20-story steel MRFs in the SAC project are used as benchmark steel MRFs. Secondly, HWs and HWBBs are designed to strengthen the benchmark steel MRFs using force-based and displacement-based methods, called HWFs and HWBBFs, respectively. Thirdly, nonlinear time history analyses are conducted to compare the structural responses of the MRFs, HWBBFs and HWFs in earthquakes. The results show the following. 1) HW seismic force demands increase as structural height increases, which may lead to uneconomical HW design. The HW seismic moment demand can be properly controlled when the building is lower than nine stories. 2) The displacement-based design method is recommended due to the benefit of identifying unfeasible component dimensions during the design process, as well as better achieving the design target displacement.
This study uses iso-geometric investigation, which is based on the non-uniform rational B-splines (NURBS) basis function, to investigate natural oscillation of bi-directional functionally graded porous (BFGP) doubly-curved shallow microshells placed on Pasternak foundations with any boundary conditions. The characteristics of the present material vary in both thickness and axial directions along the x-axis. To be more specific, a material length-scale coefficient of the microshell varies in both thickness and length directions as the material’s mechanical properties. One is able to develop a differential equation system with varying coefficients that regulate the motion of BFGP double-curved shallow microshells by using Hamilton principle, Kirchhoff–Love hypothesis, and modified couple stress theory. The numerical findings are reported for thin microshells that are spherical, cylindrical, and hyperbolic paraboloidal, with a variety of planforms, including rectangles and circles. The validity and effectiveness of the established model are shown by comparing the numerical results given by the proposed formulations with previously published findings in many specific circumstances. In addition, influences of length scale parameters, power-law indexes, thickness-to-side ratio, and radius ratio on natural oscillation responses of BFGP microshells are investigated in detail.
The accreted ice on wind turbine blades significantly deteriorates the blade aerodynamic performance and consequently the power production. The existing numerical simulations of blade icing have mostly been performed with the Eulerian approach for two-dimensional (2D) blade profiles, neglecting the possible three-dimensional (3D) rotating effect. This paper conducts a numerical simulation of rime ice accretion on a 3D wind turbine blade using the Lagrangian approach. The simulation results are validated through previously published experimental data. The icing characteristics along the blade radial direction are then investigated in detail. Significant radial airflow along the blade is observed, which demonstrates the necessity of 3D simulation. In addition, more droplets are found to impinge on the blade surface near the tip region, thereby producing severer ice accretion there. The accreted ice increases almost linearly along the blade radial direction in terms of both ice mass and maximum ice thickness.
The slab of the composite girder is usually very wide in composite cable-stayed bridges, and the main girder has an obvious shear lag. There is an axial force in the main girder due to cable forces, which changes the normal stress distribution of the composite girder and affects the shear lag. To investigate the shear lag in the twin I-shaped composite girder (TICG) of cable-stayed bridges, analytical solutions of TICGs under bending moment and axial force were derived by introducing the additional deflection into the longitudinal displacement function. A shear lag coefficient calculation method of the TICG based on additional deflection was proposed. Experiments with three load cases were conducted to simulate the main girder in cable-stayed bridges. And the stress, deflection, and shear lag coefficient obtained from the theoretical method considering additional deflection (TMAD) were verified by the experimental and finite element results. A generalized verification of a composite girder from existing references was made, indicating that the proposed method could provide more accurate results for the shear lag effect.
This paper presents a rapid and effective calibration method of mesoscopic parameters of a three-dimensional particle flow code (PFC3D) model for sandy cobble soil. The method is based on a series of numerical tests and takes into account the significant influence of mesoscopic parameters on macroscopic parameters. First, numerical simulations are conducted, with five implementation steps. Then, the multi-factor analysis of variance method is used to analyze the experimental results, the mesoscopic parameters with significant influence on the macroscopic response are singled out, and their linear relations to macroscopic responses are estimated by multiple linear regression. Finally, the parameter calibration problem is transformed into a multi-objective function optimization problem. Numerical simulation results are in good agreement with laboratory results both qualitatively and quantitatively. The results of this study can provide a basis for the calibration of microscopic parameters for the investigation of sandy cobble soil mechanical behavior.
The pipe roofing method is widely used in tunnel construction because it can realize a flexible section shape and a large section area of the tunnel, especially under good ground conditions. However, the pipe roofing method has rarely been applied in soft ground, where the prediction and control of the ground settlement play important roles. This study proposes a sliced-soil–beam (SSB) model to predict the settlement of ground due to tunnelling using the pipe roofing method in soft ground. The model comprises a sliced-soil module based on the virtual work principle and a beam module based on structural mechanics. As part of this work, the Peck formula was modified for a square-section tunnel and adopted to construct a deformation mechanism of soft ground. The pipe roofing system was simplified to a three-dimensional Winkler beam to consider the interaction between the soil and pipe roofing. The model was verified in a case study conducted in Shanghai, China, in which it provided the efficient and accurate prediction of settlement. Finally, the parameters affecting the ground settlement were analyzed. It was clarified that the stiffness of the excavated soil and the steel support are the key factors in reducing ground settlement.
