Optimizing steel consumption in segmental tunnel joints through the use of steel fiber reinforced concrete: An experimental study
Source abstract
Abstract
Large-diameter shield tunnels frequently fail due to localized joint damage, while traditional steel reinforcement often remains underutilized (elastic without yielding), leading to excessive material consumption. Steel fiber-reinforced concrete (SFRC) exhibits enhanced strength and toughness compared to conventional concrete, offering potential to mitigate localized joint damage and improve steel reinforcement efficiency. To explore this, a series of experiments were conducted, with the Steel Fiber-to-Rebar replacement ratio (SF-to-RB replacement ratio) employed as the key indicator, considering the performance index and failure behavior of the joint under both positive and negative bending moments. Additionally, the Digital Image Correlation (DIC) technique was employed to capture detailed information about the damage process. In terms of the performance index, the test results reveal that a higher SF-to-RB replacement ratio leads to a higher cracking load, increased bearing capacity, improved ductility, and enhanced joint rotational stiffness after initial cracking. Furthermore, the effect of the SF-to-RB replacement ratio differs under positive and negative moments due to distinct failure modes. With regard to the failure behavior, combined with manual and DIC measurement results, a higher SF-to-RB replacement ratio restricts the extent of damage propagation in the joint and results in smaller final crack widths. Hence, it has been demonstrated that an appropriate SF-to-RB replacement ratio can maintain the performance of the joint while reducing the overall steel consumption.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Innovative concrete reinforcement materials · Structural Behavior of Reinforced Concrete · Geotechnical Engineering and Underground Structures
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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