Search Results (10)

In the operation process of modern industrial equipment, as the core transmission component, the operation state of the gearbox directly affects the overall performance and service life of the equipment. However, the current gear operation is still faced with problems such as poor monitoring, a single detection index, and low data utilization, which lead to incomplete evaluation results. In view of these challenges, this paper proposes a shape and property integrated gearbox monitoring system based on digital twin technology and artificial intelligence, which aims to realize real-time fault diagnosis, performance prediction, and the dynamic visualization of gear through virtual real mapping and data interaction, and lays the foundation for the follow-up predictive maintenance application. Taking the QPZZ-ii gearbox test bed as the physical entity, the research establishes a five-layer architecture: functional service layer, software support layer, model integration layer, data-driven layer, and digital twin layer, forming a closed-loop feedback mechanism. In terms of technical implementation, combined with HyperMesh 2023 refinement mesh generation, ABAQUS 2023 simulates the stress distribution of gear under thermal fluid solid coupling conditions, the Gaussian process regression (GPR) stress prediction model, and a fault diagnosis algorithm based on wavelet transform and the depth residual shrinkage network (DRSN), and analyzes the vibration signal and stress distribution of gear under normal, broken tooth, wear and pitting fault types. The experimental verification shows that the fault diagnosis accuracy of the system is more than 99%, the average value of the determination coefficient (R²) of the stress prediction model is 0.9339 (driving wheel) and 0.9497 (driven wheel), and supports the real-time display of three-dimensional cloud images. The advantage of the research lies in the interaction and visualization of fusion of multi-source data, but it is limited to the accuracy of finite element simulation and the difficulty of obtaining actual stress data. This achievement provides a new method for intelligent monitoring of industrial equipment and effectively promotes the application of digital twin technology in the field of predictive maintenance. Full article

During deep foundation pit construction, slight improper operations may lead to excessive deformation, resulting in engineering accidents. Therefore, how to accurately predict the deformation of the deep foundation pit is of significant importance. With advancements in artificial intelligence technology, machine learning has been utilized to learn and simulate complex nonlinear relationships among various factors influencing foundation pit deformation. Prediction accuracy is significantly improved, and the dynamic trend of foundation pit deformation is accurately grasped to curb the risk of safety accidents. This paper systematically reviews the current applications of machine learning in deep foundation pit deformation prediction. The fundamental principles of machine learning models, including neural networks, support vector machines, and Bayesian networks, are elaborated in the context of their application to deep foundation pit deformation prediction. The application effects of various machine learning models in predicting deep foundation pit supporting structure deformation, surrounding surface settlement, and assessing foundation pit risks are summarized. The limitations and future development prospects of current machine learning models for deformation prediction in deep foundation pit construction are discussed. The research results offer valuable insights for the application and advancement of machine learning in the deep foundation pit deformation prediction field. Full article

Underground space development has significantly increased the depth, scale, and complexity of foundation pit engineering. However, monitoring systems lack mechanical analysis models and fail to predict and control construction risks. Additionally, the foundation pit model could not be updated based on on-site observed data, leading to inaccurate predictions. This study proposes a DT modeling framework for foundation pits, which is used to simulate, predict, and control the risks associated with the entire excavation process. Consequently, based on the DT modeling framework, a DT foundation pit model (DTFPM) was established using modeling and updating algorithms. This study summarizes and identifies the key modeling parameters of foundation pits. A parametric modeling algorithm based on ABAQUS (v2020) was developed to drive the excavation pit modeling process within seconds. Furthermore, an inverse analysis optimization algorithm based on genetic algorithms (GA) and real-time observed deformation was employed to update the elastic modulus of the soil. The algorithm supports parallel computing and can converge within 10 generations. The prediction error of the model after inverse analysis can be reduced to within 10%. Finally, the authors applied DTFPM to establish an intelligent monitoring system. The focus is on real-time and predictive warnings based on the monitoring deformation of the current construction step and the updated model. This study analyzes a Beijing project case to verify the effectiveness of the system, demonstrating the practical application of the proposed method. The results showed that the DTFPM could accurately simulate the deformation behavior of the foundation pit. The system could provide more timely and accurate safety warnings. The proposed method can potentially contribute to the intelligent construction of foundation pits in the future, both theoretically and practically. Full article

Currently, subway and underground engineering projects are vital for alleviating urban congestion and enhancing citizens’ quality of life. Among these, excavation engineering for foundation pits involves the most accidents in geotechnical engineering. Although there are various construction methods, most face issues such as a large footprint, high investments, resource waste, and low mechanization. Addressing these, this paper focuses on a subway foundation pit project in Guangzhou using mechanical shaft sinking technology. Using intelligent cloud monitoring, we analyzed the stress–strain patterns of the cutting edge and segments. The results showed significant improvements in construction efficiency, cost reduction, safety, and resource conservation. Based on this work, this paper makes the following conclusions: (1) The mechanical shaft sinking method offers advantages such as small footprint, high mechanization, minimal environmental impact, and cost-effectiveness. The achievements include a 22.22% reduction in construction time, a 20.27% decrease in investment, and lower worker risk. (2) Monitoring confirmed that all cutting edge and segment values remained safe, demonstrating the method’s feasibility and rationality. (3) Analyzing shaft monitoring data and field uncertainties, this study proposes recommendations for future work, including precise segment lowering control and introducing high-precision total stations and GPS technology to mitigate tunneling and assembly inaccuracies. The research validates the mechanical shaft sinking scheme’s scientific and logical nature, ensuring safety and contributing to technological advancements. It offers practical insights, implementable suggestions, and significant economic benefits, reducing project investment by RMB 41,235,600. This sets a benchmark for subway excavation projects in South China and beyond, providing reliable reference values. Furthermore, the findings provide valuable insights and guidance for industry peers, enhancing overall efficiency and sustainable development in subway construction. Full article

Currently, there are many enterprises involved in extracting and processing of primary raw materials. The danger of working in this industry consists in the formation of cracks in rocks of the pit side slopes, which can lead to destruction. This article discusses the existing systems for monitoring the pit collapse prevention. The most promising is the use of systems with fiber-optic sensors. However, use of these systems is associated with some difficulties due to high costs, low noise immunity, and in some cases, the requirement for additional equipment to improve the reliability of measurements. A completely new method of processing the data from a fiber-optic sensor that simplifies the design and reduces the cost of the device is proposed considering the experience of previous developments. The system uses artificial intelligence, which improves the data processing. The theoretical part is dedicated to the development of foundations, and the analysis of the nonlinear properties of the physical and mathematical model of optical processes associated with the propagation of an electromagnetic wave in a fiber-optic material was developed. The results of experimental and theoretical applied research, which are important for the development of fiber-optic systems for monitoring the pit collapse prevention, are presented. The dependences of optical losses and the number of pixels on the dis-placement were obtained. The accuracy of the method corresponds to the accuracy of the device by which it is calibrated and is 0.001 mm. The developed hardware-software complex is able to track the rate of changing the derivative of the light wave intensity in time, as well as changing the shape of the spot and transition of pixels from white to black. Full article

The construction of a subway deep foundation pit is complex and risky, thus multiple safety risk factors bring great challenges to evaluating the safety status accurately. Advanced monitoring technology equipment could obtain a large number of monitoring data, and how integrating complex and diversified monitoring data to assess the safety risk of foundation pits has become a new problem. Therefore, an intelligent multi-source fusion assessment model is proposed. This model is mainly used for solving risk probability distribution, deep learning, and intelligent prediction of monitoring indicators, and then evaluating safety status by fusing various parameters of multiple indicators. Thus, based on the data of deep learning and the measured multivariate data, the dynamic risk during foundation pit construction can be obtained. Moreover, a typical case study was performed through monitoring and carrying out the risk assessment which is located at the Martyrs’ Lingyuan Station of Jinnan Metro Line R2, China. In this case, the PSO-SVM and LSTM models are used to predict the deformation trend, and the monitoring data is reliable with high precision. After multi-index fusion model calculation, the results show that the foundation pit structure is in a safe state, and the evaluation situation is basically consistent with the site. Consequently, the prediction of the new multi-source intelligent fusion risk assessment method is convincing. Full article

Timely risk information acquisition and diagnosis during foundation pit excavation (FPE) processes are vital for ensuring the safe and effective construction of underground urban infrastructures. Unfortunately, diverse geological and hydrogeological conditions and complex shapes of the foundation pit create barriers for reliable FPE risk prognosis and control. Furthermore, typical support systems during FPE use temporary measures, which have limited capacity to confront excessive loads, large deformations, and seepage. This study aims to establish an intelligent risk prognosis and control framework based on digital twin (DT) for ensuring safe and effective FPE processes. Previous studies have conducted extensive experimental and numerical analyses for examining unsafe conditions during FPE. How to enable intelligent risk prognosis and control of tedious FPE processes by integrating physics-based models and sensory data collected in the field is still challenging. DT could help to establish the interaction and feedback mechanisms between the physical and virtual space. In this study, the authors have established a DT model that consists of a physical space model and a high-fidelity physics-based model of a foundation pit in virtual space. As a result, a mechanism for effective acquisition and fusion of heterogeneous information from both physical and virtual space is established. Then, the authors proposed an integrated model and data-driven approach for examining safety risks during FPE. In the end, the authors have validated the proposed method through a case study of the FPE of the Wuhan Metro Line. The results show that the proposed method could provide theoretical and practical support for future intelligent FPE. Full article

As the scale of foundation pit projects of subway stations in Shenzhen becomes larger, and the construction constraints become more and more complex, there is an urgent need for intelligent monitoring and safety management of foundation pits. In this study, an integrated intelligent approach for monitoring and management of a deep foundation pit in a subway station was proposed and a case study based on the Waterlands Resort East Station Project of Shenzhen Metro Line 12 was used for validation. The present study first proposed the path of intelligent foundation pit engineering. Based on geotechnical survey and building information modeling, a three-dimensional transparent geological model of foundation pit was constructed. Multi-source sensing technologies were integrated, including micro electromechanical system sensing technology, Brillouin optical frequency domain analysis sensing technology, an unmanned aerial vehicle and machine vision for real-time high-precision wireless monitoring of the foundation pit. Moreover, machine learning models were developed for predicting key parameters of foundation pits. Finally, a digital twin integrated platform was developed for the management of the subway foundation pit in both construction and maintenance phases. This typical case study is expected to improve the construction, maintenance and management level of foundation pits in subway stations. Full article

The safe and sustainable design of rock slopes, open-pit mines, tunnels, foundations, and underground excavations requires appropriate and reliable estimation of rock strength and deformation characteristics. Cohesion (𝑐) and angle of internal friction (𝜑) are the two key parameters widely used to characterize the shear strength of materials. Thus, the prediction of these parameters is essential to evaluate the deformation and stability of any rock formation. In this study, four advanced machine learning (ML)-based intelligent prediction models, namely Lasso regression (LR), ridge regression (RR), decision tree (DT), and support vector machine (SVM), were developed to predict 𝑐 in (MPa) and 𝜑 in (°), with P-wave velocity in (m/s), density in (gm/cc), UCS in (MPa), and tensile strength in (MPa) as input parameters. The actual dataset having 199 data points with no missing data was allocated identically for each model with 70% for training and 30% for testing purposes. To enhance the performance of the developed models, an iterative 5-fold cross-validation method was used. The coefficient of determination (R²), mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and a10-index were used as performance metrics to evaluate the optimal prediction model. The results revealed the SVM to be a more efficient model in predicting 𝑐 (R² = 0.977) and 𝜑 (R² = 0.916) than LR (𝑐: R² = 0.928 and 𝜑: R² = 0.606), RR (𝑐: R² = 0.961 and 𝜑: R² = 0.822), and DT (𝑐: R² = 0.934 and 𝜑: R² = 0.607) on the testing data. Furthermore, to check the level of accuracy of the SVM model, a sensitivity analysis was performed on the testing data. The results showed that UCS and tensile strength were the most influential parameters in predicting 𝑐 and 𝜑. The findings of this study contribute to long-term stability and deformation evaluation of rock masses in surface and subsurface rock excavations. Full article

In order to effectively predict the dynamic displacement and disaster, according to the analysis of the influencing parameters affecting the deformation of a subway foundation pit supported by piles (walls), the rough set attribute reduction method (RSARM) and the average influence value algorithm (AIVA) are used to simplify the influencing factors of foundation pit deformation. Those simplified factors are taken as the input of the ELM, with the output being the displacement of the foundation pit. Finally, the IPM of foundation pit displacement derived from the ELM is obtained, which is finally used for engineering practice. The results show that it is feasible to simplify the influencing factors of the deformation of the foundation pit by RSARM and AIVA. The proposed IPM of foundation pit displacement has high accuracy and good generalization ability, which can be used for the deformation prediction. Full article