A Study on the Ultimate Span of a Concrete-Filled Steel Tube Arch Bridge

Reinforced concrete slabs are widely used in building structures due to their economic, durable, and aesthetic advantages. The determination of their ultimate strength often hinges on punching shear strength. Presently, methods such as closed hoops, steel bending, and fiber reinforcement are employed to enhance punching shear strength, with fiber reinforcement gaining popularity due to its ease of implementation and efficacy in improving concrete durability. This study introduces a novel approach employing six machine learning algorithms rooted in decision trees and decision tree-based ensemble learning to predict punching shear strength in steel fiber-reinforced concrete slabs. To overcome experimental data limitations, a data augmentation approach based on the Gaussian mixture model is employed. The validation of the data augmentation is conducted through “synthetic training—real testing” and “real training—real testing”. Additionally, the best machine learning model is analyzed for explainability using Shapley Additive exPlanation (SHAP). Results demonstrate that the proposed data augmentation method effectively captures the original data distribution, enhancing the robustness and accuracy of the machine learning model. Moreover, SHAP provides better insights into the features influencing punching shear strength. Thus, the proposed data enhancement model offers a reliable approach for modeling small experimental datasets in structural engineering.

Section: Introductionmentioning

confidence: 99%

Accurate Prediction of Punching Shear Strength of Steel Fiber-Reinforced Concrete Slabs: A Machine Learning Approach with Data Augmentation and Explainability

Cheng,

Taffese,

2024

“…Yi and Liu [12], based on the basic principles of the frequency method and employing mathematical elimination, derived a tension calculation formula that considers bending stiffness but does not explicitly reflect it, offering a simple and rapid calculation method, albeit with ambiguity regarding cable length selection. Wu et al [13] proposed an optimization method based on the response surface method, which provides a new idea for the analysis of large-span arch bridges.…”

Section: Introductionmentioning

confidence: 99%

Research on Cable Tension Prediction Based on Neural Network

Zhang,

2024

Conventional methods for calculating tension currently suffer from an excessive simplification of boundary conditions and a vague definition of effective cable length, both of which cause inaccurate cable tension calculations. Therefore, this study utilizes bridge field data to establish a BP neural network for tension prediction, with design cable length, line density, and frequency as the input parameters and with cable tension as the output parameter. After disregarding the selection of effective cable length and innovatively integrating the particle swarm optimization–back propagation (PSO-BP) neural network for tension prediction, it is found that the MAPE between the predicted results of the BP neural network and the actual tension values is 7.93%. After optimization using the particle swarm optimization algorithm, the mean absolute percentage error (MAPE) of the neural network prediction is reduced to 2.78%. Both of these values significantly outperform those obtained from the theoretical equations of string vibration. Moreover, the MAPE of PSO-BP also surpasses that of the optimized calculation formulas in the literature. Utilizing the PSO-BP neural network for tension prediction avoids inaccuracies in tension calculation caused by an excessive simplification of boundary conditions and a vague definition of effective cable length; thus, it possesses certain engineering practical value.

“…With the rapid expansion of infrastructure development, there is more and more research related to concrete bridges, such as for the ultimate span of steel tube concrete bridges [1], the application of holographic projection technology to bridge construction [2], and the bearing capacity of short concrete columns [3], while the research in this paper is about the apparent defect segmentation of concrete bridges. During the use of concrete bridges, frequent defects include crack, spallation, efflorescence, exposedbar, etc., which will jeopardize the stability and longevity of the bridge structure [4].…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Apparent Multi-Defect Images of Concrete Bridges Based on PID Encoder and Multi-Feature Fusion

Liao,

Huang,

Yin

2024

To address the issue of insufficient deep contextual information mining in the semantic segmentation task of multiple defects in concrete bridges, due to the diversity in texture, shape, and scale of the defects as well as significant differences in the background, we propose the Concrete Bridge Apparent Multi-Defect Segmentation Network (PID-MHENet) based on a PID encoder and multi-feature fusion. PID-MHENet consists of a PID encoder, skip connection, and decoder. The PID encoder adopts a multi-branch structure, including an integral branch and a proportional branch with a “thick and long” design principle and a differential branch with a “thin and short” design principle. The PID Aggregation Enhancement (PAE) combines the detail information of the proportional branch and the semantic information of the differential branch to enhance the fusion of contextual information and, at the same time, introduces the self-learning parameters, which can effectively extract the information of the boundary details of the lesions, the texture, and the background differences. The Multi-Feature Fusion Enhancement Decoding Block (MFEDB) in the decoding stage enhances the information and globally fuses the different feature maps introduced by the three-channel skip connection, which improves the segmentation accuracy of the network for the background similarity and the micro-defects. The experimental results show that the mean Pixel accuracy (mPa) and mean Intersection over Union (mIoU) values of PID-MHENet on the concrete bridge multi-defect semantic segmentation dataset improved by 5.17% and 5.46%, respectively, compared to the UNet network.