The digital twin, a concept that aims to establish a real-time mapping between physical space and virtual space, can be used for real-time analysis, reliability assessment, predictive maintenance, and design optimization of products. This article presents an enabling technology named shape–performance integrated digital twin (SPI-DT) and takes a boom crane as an example to illustrate how to design the SPI-DT step by step for the structural analysis of complex heavy equipment. The SPI-DT contains different types of models, such as an analytical model, a numerical model, and an artificial intelligence (AI) model. In addition, it leverages multisource dynamic data obtained by placing different sensors at multiple measurement positions. In the SPI-DT, the AI model plays a central role, invoking the numerical model and sensor data as the input to predict the structural performance of key components of heavy equipment, while the analytical model analyzes the structure of noncritical components with sensor data as input. This significantly improves the computational efficiency of the digital twin used for the structural analysis of complex heavy equipment, making the digital twin computationally affordable, and thus can be used for the safety assessment and damage protection of the equipment in the operation, as well as the design optimization of next-generation products. Moreover, to visually demonstrate the models and data in the SPI-DT, a three-dimensional application used to display and record the shape and performance information in real time during the operation of the boom crane is developed.

Data imbalance is a thorny issue in machine learning. SMOTE is a famous oversampling method of imbalanced learning. However, it has some disadvantages such as sample overlapping, noise interference, and blindness of neighbor selection. In order to address these problems, we present a new oversampling method, OS-CCD, based on a new concept, the classification contribution degree. The classification contribution degree determines the number of synthetic samples generated by SMOTE for each positive sample. OS-CCD follows the spatial distribution characteristics of original samples on the class boundary, as well as avoids oversampling from noisy points. Experiments on twelve benchmark datasets demonstrate that OS-CCD outperforms six classical oversampling methods in terms of accuracy, F1-score, AUC, and ROC.

One main challenge in tunnel constructions is to predict the tunnel geological conditions without excavation to ensure safety during the construction process. This paper proposes a data-driven framework for real-time interpreting the operating data of tunnel boring machines (TBMs) without interrupting tunneling operations, and eventually automate the tunneling operation. In this framework, we first convert the indexes of the original data from discontinuous operating time to continuous operating displacement. After screening outliers, to more exhaustively explore the inherent characteristics of TBM operating data, we then augment features by using the first-order and the second-order difference information. There are two main concerns for developing a desired geological-type predictor: 1) since multiple geological types could coexist in one tunnel section, the predictor should have multiple outputs; and 2) since the geological types are specified by the values of 7 kinds of physical-mechanical indexes of geological types, this geological characteristic should also be encoded into the predictor’s structure. Therefore, we design a feedforward multiple-output artificial neural network (ANN) with two hidden layers as the predictor, where the second hidden layer has 7 nodes that correspond to 7 kinds of physical-mechanical indexes. The experimental results show that: 1) the feature augmentation (FA) method indeed improves the prediction performance; 2) the ANN predictor has the best performance on the test set when the second hidden layer has 7 nodes; 3) the proposed ANN predictor outperforms many widely-used learning models, e.g., XGboost, random forest (RF) and support vector regression (SVR); and 4) the predictor is capable of accurately predicting the geological types of stratum.

NVIDIA Omniverse offers a unified platform for 3-D production pipelines based on the digital twins of real physical systems. The Internet of Things facilitates the acquisition of Omniverse data from different information sources, including finite-element model simulations and various sensors, and such data describe the responses of physical systems. According to their response description accuracies, these multisource data can be divided into different fidelity levels. High-fidelity (HF) data describe responses of the given system accurately but are costly to obtain. In contrast, low-fidelity (LF) data are inexpensive but often do not reach the desired accuracy level. Multifidelity data fusion (MDF) aims to use massive LF data and small amounts of HF data to develop the digital twin of a real physical system to produce accurate digital system responses. In this article, we propose a novel generative adversarial network for MDF. Experimental results show that the proposed model performs better than the state-of-the-art methods without any specific assumptions regarding the data distribution or data structure and has higher stability when addressing varying amounts of HF and LF data, especially in cases with very few HF data.