Comparison and a case study of test methods for sleeve grouting fullness in precast bridge piers

Zhou, Zhipeng; Wu, Hongyu; Tan, Chee-Ghuan; Xing, Ya Zhe; Gao, Rundong

doi:10.1201/9780429343292-128

Cited by 5 publications

(3 citation statements)

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“…According to the detection principle, grouted sleeve tests fall into two categories: micro-damage and nondestructive methods. The embedded wires [12], damper sensors [13], and endoscopes [14] are used in destructive tests and can directly contact grouting defects. The buried damper sensors have different energy attenuation values in the different media (air, water and grout).…”

Section: Introductionmentioning

confidence: 99%

Combined two-level guided wave structural health monitoring strategy using multifeature integration and machine learning: application to early-age grouted sleeves

Liu,

Yi,

et al. 2023

Smart Mater. Struct.

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Structural health monitoring of grouted sleeves is one of the assembly industry’s huge challenges. In this study, a combined two-level damage detection was introduced. It comprises defect classification (healthy, rebar eccentricity, and grout leakage) and severity evaluation for early-age grouted sleeves using guided waves. Multiple features (MF) from time-, frequency-, and time-frequency domains were extracted and defined according to the diverse defects and ages of grouted sleeves to represent complex damage characteristics. Moreover, the egret swarm algorithm optimization–extreme learning machine (ESAO-ELM) models were proposed to avoid the influence of subjective experience and judgment from experts. ESAO optimized the initial random parameters (input weights and hidden layer bias) of ELM. Then, two MF-ESAO-ELM models were trained for two-level damage detection on the experimental dataset. The performance of the proposed models was comprehensively evaluated using accuracy, recall, precision, and confusion matrix. MF-ESAO-ELM performs better than ELM and PSO-ELM in accuracy. In this strategy, the defect classification model works in the outer layer to distinguish the state and types of defects of grouted sleeves (healthy, eccentric, or leakage). In comparison, the inner layer starts predicting the severity only if the defect type is leakage. MF-ESAO-ELM offers advantages in terms of accuracy, strategy, and calculation time.

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Section: Introductionmentioning

confidence: 99%

Combined two-level guided wave structural health monitoring strategy using multifeature integration and machine learning: application to early-age grouted sleeves

Liu,

Yi,

et al. 2023

Smart Mater. Struct.

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“…Conventional methods to identify connection defects in prefabricated structures include embedded testing (embedded sensor and embedded wire drawing), 4 core drilling, 4 and non-destructive testing (X-ray; ultrasonic wave; impact-echo, ground penetrating radar-based testing). 5,6 These methods face challenges that make then inconvenient for large-scale field application.…”

Section: Introductionmentioning

confidence: 99%

Deep transfer learning for connection defect identification in prefabricated structures

Tang

Xie

2022

Structural Health Monitoring

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Defect and damage identification is a crucial task in structural health monitoring (SHM) systems. Recent advances in deep neural networks (DNNs) show success in identification from data for a wide range of SHM systems. However, this approach faces challenges in terms of robustness and scalability with respect to data scarcity. Data collection for the training of DNNs from both the field and laboratory experiments is costly. To address this issue, we employ transfer learning (TL) through the use of deep convolutional neural networks (CNNs) for defect identification in the context of a sensor network’s vibration data. A deep TL (DTL) paradigm is used herein so that a pretrained CNN, primarily trained for generalized defect identification tasks where sufficient training data exist (source domain), can be re-trained partially (fine-tuned) as a later secondary process that targets this application domain (target domain) specifically. Different DTL cases are compared, and training data are enhanced with numerical simulation data. The efficacy and robustness of this method are demonstrated on defect identification for full-scale prefabricated concrete shear wall structures with different levels of data scarcity. This method utilizes dynamic responses collected using a sensor network. This is an extension of deep learning vision for non-vision tasks. Defect features are extracted from the dataset of dynamic responses using this DTL frame. Experimental results show that this approach can improve identification models on datasets with few samples.

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“…Another attractive research avenue on grouting defects concerns their efficient detection to enhance the structural monitoring and maintenance of the connection. Significant research efforts have been invested in developing accurate detection methods of defects within the connection using nondestructive testing (NDT) [39][40][41][42][43][44]. Feng et al [45] proposed a time-reversal ultrasonic waves signal analysis to detect the change in the grout compactness in the sleeve.…”

Section: Introductionmentioning

confidence: 99%

Experimental and numerical assessment of the location-based impact of grouting defects on the tensile performance of the fully grouted sleeve connection

Kahama

Xie

Lü

et al. 2022

Frattura Ed Integrità Strutturale

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The presence of grouting defects emanating from construction operations constitutes a major threat to the structural integrity of the grouted sleeve connection of precast concrete members. This work presents a location-based assessment of the impact of grouting defects on the tensile performance of the grouted sleeve connection. Twenty-two specimens with different configurations of defects were subjected to a uniaxial tensile experiment. Corresponding numerical models were proposed, validated and used to conduct a sensitivity analysis of the connection to the defect's location while considering different design confinements of grouting materials. Experimental and numerical studies revealed the following outcome: In consideration of confinement parameters, when the defect of size 3d is located in the mid-span anchorage length, the variation of the sleeve-to-bar diameter (ds/d) from 2.66 (lower design limit) to 3.55 (upper design limit) changed the drop in the ultimate capacity from 19% to 44% below the connection's design requirement. The governing parameters of the grout-bar bond stiffness were the defect's location and degree of confinement. This work proposes a theoretical diagnosis model and a risk assessment catalogue as a promising step toward establishing a computerized diagnosis model of the defective connection to enlighten rational maintenance actions in actual construction.

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Comparison and a case study of test methods for sleeve grouting fullness in precast bridge piers

Cited by 5 publications

References 1 publication

Combined two-level guided wave structural health monitoring strategy using multifeature integration and machine learning: application to early-age grouted sleeves

Combined two-level guided wave structural health monitoring strategy using multifeature integration and machine learning: application to early-age grouted sleeves

Deep transfer learning for connection defect identification in prefabricated structures

Experimental and numerical assessment of the location-based impact of grouting defects on the tensile performance of the fully grouted sleeve connection

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