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Real-Time Structural Damage and Impact Identification, and Life Prediction Using Advanced Sensor Systems and Methods

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: 15 June 2026 | Viewed by 2600

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Special Issue Editors

Prof. Dr. Junzhou Huo

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Guest Editor

School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China
Interests: innovative design of major equipment; damage mechanisms of complex structures; intelligent in situ monitoring; machine vision technology
Special Issues, Collections and Topics in MDPI journals

Dr. Laikuang Lin

E-Mail Website
Guest Editor

School of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China
Interests: structural design of underground engineering equipment; online detection of tool status; tool change robot design; machine vision; shield big data mining and analysis

Dr. Jingyu Zhai

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Guest Editor

School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China
Interests: dynamics analysis of rotating machinery; advanced damping and vibration control technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Structural damage can significantly affect the service life of equipment, making the monitoring of structural damage and impact loads particularly important. Recently, there has been growing interest in intelligent condition monitoring of structures. Intelligent monitoring of structural states primarily utilizes sensors to track changes in the damage conditions of various structures, enabling the identification and warning of current damage, as well as the prediction of the structure's remaining healthy life.

This Special Issue aims to compile original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the field of structural damage identification and prediction.

Potential topics include, but are not limited to, the following:

Visual impairment recognition;
Online prediction of structural damage;
Structural wear and life prediction;
Structural health monitoring based on multi-sensor systems;
Load identification and warning;
Load advance prediction;
Structural impact identification and early warning;
Crack initiation monitoring;
Crack growth prediction;
Structural fatigue life monitoring;
Development of new sensors for crack detection.

Prof. Dr. Junzhou Huo
Dr. Laikuang Lin
Dr. Jingyu Zhai
Guest Editors

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Keywords

sensing
structural damage
multi-sensor fusion
structural life prediction
impact monitoring
load prediction
crack detection
crack initiation and propagation

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Published Papers (3 papers)

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Research

19 pages, 4882 KB

Open AccessArticle

Damage State Recognition and Quantification Method for Shield Machine Hob Based on Deep Forest

by Huawei Wang, Qiang Gao, Sijin Liu, Peng Liu, Xiaotian Wang and Ye Tian

Sensors 2026, 26(5), 1586; https://doi.org/10.3390/s26051586 - 3 Mar 2026

Viewed by 449

Abstract

The damage status of shield machine disc cutters directly impacts the safety and efficiency of tunnelling projects. Current manual inspection methods involve high risks and low efficiency, while existing detection methods suffer from low accuracy and poor real-time performance in complex environments, often lacking quantitative analysis capabilities. To address these issues, this paper proposes an intelligent identification and quantitative assessment method for disc cutter damage based on the Deep Forest (DF) model. First, an eddy current sensor calibration platform was established, and a mapping relationship between output voltage and actual wear was developed through piecewise fitting to achieve precise wear quantification. In the data preprocessing stage, signal quality was improved via filtering, and typical damage features such as edge chipping, cracks, and eccentric wear were extracted using pulse edge detection. These feature segments were then resampled to construct the model training dataset. The DF model utilizes a hierarchical ensemble structure to mine data correlations, enabling accurate identification of four states: normal, edge chipping, eccentric wear, and cracks. Simultaneously, a DF regression model was employed to provide continuous quantitative predictions of damage size. Experimental results show that the classification model achieved accuracies of 98%, 96%, and 96% on the training, validation, and test sets, respectively, with weighted average F1-scores exceeding 0.96. The regression model achieved a coefficient of determination (R²) of 0.9940 and a root mean square error (RMSE) of 0.4051 on the test set. Both models demonstrate excellent performance and generalization, achieving full coverage from “qualitative state identification” to “quantitative wear assessment,” thereby providing reliable decision support for cutter maintenance and replacement. Full article

(This article belongs to the Special Issue Real-Time Structural Damage and Impact Identification, and Life Prediction Using Advanced Sensor Systems and Methods)

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26 pages, 10802 KB

Open AccessArticle

Indirect Vision-Based Localization of Cutter Bolts for Shield Machine Cutter Changing Robots

by Sijin Liu, Zilu Shi, Yuyang Ma, Yang Meng, Jun Wang, Qianchen Sha, Yingjie Wei and Xingqiao Yu

Sensors 2025, 25(24), 7685; https://doi.org/10.3390/s25247685 - 18 Dec 2025

Viewed by 809

Abstract

In operations involving the replacement of shield machine disc cutters, challenges such as limited space, poor lighting, and slurry contamination frequently lead to occlusions and incomplete data when using direct point cloud-based localization for disc cutter bolts. To overcome these issues, this study introduces an indirect visual localization technique for bolts that utilizes image-point cloud fusion. Initially, an SCMamba-YOLO instance segmentation model is developed to extract feature surface masks from the cutterbox. This model, trained on the self-constructed HCB-Dataset, delivers a mAP50 of 90.7% and a mAP50-95 of 82.2%, which indicates a strong balance between its accuracy and real-time performance. Following this, a non-overlapping point cloud registration framework that integrates image and point cloud data is established. By linking dual-camera coordinate systems and applying filtering through feature surface masks, essential corner coordinates are identified for pose calibration, allowing for the estimation of the three-dimensional coordinates of the bolts. Experimental results demonstrate that the proposed method achieves a localization error of less than 2 mm in both ideal and simulated tunnel environments, significantly enhancing stability in low-overlap and complex settings. This approach offers a viable technical foundation for the precise operation of shield disc cutter changing robots and the intelligent advancement of tunnel boring equipment. Full article

(This article belongs to the Special Issue Real-Time Structural Damage and Impact Identification, and Life Prediction Using Advanced Sensor Systems and Methods)

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19 pages, 20264 KB

Open AccessArticle

Metal Crack Length Prediction and Sensor Fault Self-Diagnosis Method Based on Deep Forest

by Qiang Gao, Yang Meng, Hua Li, Bowen Yang and Junzhou Huo

Sensors 2025, 25(23), 7149; https://doi.org/10.3390/s25237149 - 23 Nov 2025

Cited by 1 | Viewed by 767

Abstract

Metal structures develop cracks under fatigue loading, which subsequently propagate. The size of the cracks directly affects the fatigue life of the structure. Accurate prediction of crack lengths under various loading conditions is crucial for the safe service of structures. And the crack length has a significant influence on the local strain of the structure. In this paper, finite element analysis (FEA) is used to extract strain data from various measurement points of compressive and tensile (CT) specimens under different loading conditions. The Deep Forest (DF) model is employed to optimize the training of the data. Compensation is applied to the measured dynamic strain data for predicting crack length. Experimental results show that multi-dimensional input signals in the XY plane can accurately predict crack length. Additionally, based on the Pearson correlation coefficient, this paper proposes a self-diagnostic coefficient for strain sensors. Combined with the DF model, it enables self-diagnosis of the strain sensor. The proposed crack length prediction and strain sensor self-diagnosis methods enhance the intelligence level of crack state monitoring to some extent. Full article

(This article belongs to the Special Issue Real-Time Structural Damage and Impact Identification, and Life Prediction Using Advanced Sensor Systems and Methods)

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Real-Time Structural Damage and Impact Identification, and Life Prediction Using Advanced Sensor Systems and Methods

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