The large vibrations of stay cables pose significant challenges to the structural performance and safety of cable-stayed bridges. While magnetorheological dampers (MRDs) have emerged as an effective solution for suppressing these vibrations, establishing accurate forward and inverse mapping models for MRDs to facilitate effective semi-active control of cable vibrations remains a formidable task. To address this issue, the current study proposes an innovative strategy that leverages Long Short-Term Memory (LSTM) neural networks for MRD modeling, thus enhancing semi-active control of stay cable vibrations. A high-fidelity data set accurately capturing the MRD dynamics is first generated by coupling finite element analysis and computational fluid dynamic approach. The obtained data set is then utilized for training LSTM-based forward and inverse mapping models of MRD. These LSTM models are subsequently integrated into dynamic computational models for effectively suppressing the stay cable vibrations, culminating in an innovative semi-active control strategy. The feasibility and superiority of the proposed strategy are demonstrated through comprehensive comparative analyses with existing passive, semi-active and active control methodologies involving sinusoidal load, Gaussian white noise load and rain–wind induced aerodynamic load scenarios, paving the way for novel solutions in semi-active vibration control of large-scale engineered structures.
Seismic resistance systems for small and mid-span girder bridges often lacks hierarchically repeatable earthquake resistance, leading to challenging and time-consuming post-earthquake repairs. This research introduces a novel quasi-floating seismic resistance system (QFSRS) with hierarchically sacrificial components to enable multiple instances of earthquake resistance and swift post-earthquake restoration. Finite element modeling, a numerical probabilistic approach, and earthquake-simulating shake-table tests identified highly sensitive parameters from the QFSRS to establish theoretical equations describing the mechanical model and working mechanism of the system. The results indicate that the working mechanism of the QFSRS under seismic conditions aligns with the theoretical design, featuring four hierarchically sacrificial seismic stages. Specifically, under moderate (0.3g) or higher seismic conditions, QFSRS reduced relative displacement between piers and beams by 55.15% on average. The strain at pier bases increased 6.17% across all seismic scenarios, significantly enhancing bridge seismic performance. The QFSRS provides resilient and restorable earthquake resistance for girder bridges.
Underground group tanks (UGTs) for edible oil offer benefits in land conservation, ecological sustainability, and oil quality preservation. However, ensuring their structural integrity is a critical concern. This study investigates the mechanical behavior and stability of tank walls with inner steel plate lining in the empty tank, employing both full-scale tests and numerical simulations. Parameters such as internal forces, circumferential deformation, and wall stability under earth pressure were comprehensively examined. Findings reveal that the circumferential internal forces in walls proximal to the junction are more influenced by the junction and adjacent tank walls than those in walls located further away. The numerical results deviate by only 7.7% and 13.3% from the experimental results, verifying the efficacy and accuracy of the numerical model employed. Additionally, it was determined that for tank walls with heights below 5 m, the internal force can be computed using retaining wall force calculations; for greater heights, arch action force calculations are more suitable. Based on stability analysis, a formula for assessing the stability of double-layer, heterogeneous material group tank walls under earth pressure is introduced. It is advised that the thickness of the concrete tank wall should exceed 150 mm to ensure structural stability. These findings offer valuable insights into the rational design of UGTs.
Fiber reinforced polymer (FRP) retrofits are widely used to strengthen structures due to their advantages such as high strength-to-weight ratio and durability. However, the bond strength between FRP and masonry is crucial for the success of these retrofits. Limited data exists on the shear bond between FRP composites and masonry substrates, necessitating the development of accurate prediction models. This study aimed to create machine learning models based on 1583 tests from 56 different experiments on FRP-masonry bond strength. The researchers identified key factors influencing failure load and developed machine learning models using three algorithms. The proposed models outperformed an existing model with up to 97% accuracy in predicting shear bond strength. These findings have significant implications for designing safer and more effective FRP retrofits in masonry structures. The study also used Sobol sensitivity analysis and SHapley Additive exPlanations (SHAP) analysis to understand the machine learning models, identifying key input features and their importance in driving predictions. This enhances model transparency and reliability for practical use.
The surface chloride concentration of concrete is a critical factor in determining the service life of concrete in tidal environments. This study aims to identify an effective Machine Learning (ML) model for predicting and assessing surface chloride concentration in such conditions. Using a database that includes 12 input variables and 386 samples of surface chloride concentration in seawater-exposed concrete, the study evaluates the predictive performance of nine ML models. Among these models, the Gradient Boosting (GB) model, using default hyperparameters, demonstrates the best performance, achieving a coefficient of determination (R2) of 0.920 and a root mean square error of 0.103% by weight of concrete for the testing data set. Furthermore, an Excel file based on the GB model is created to estimate surface chloride concentration, simplifying the mix design process according to concrete durability requirements. The Shapley additive explanation values and partial dependence plot one dimension offer a detailed analysis of the impact of the 12 variables on surface chloride concentration. The four most influential factors are, in descending order, fine aggregate content, exposure time, annual mean temperature, and coarse aggregate content. Specifically, surface chloride concentration increases linearly with prolonged exposure time, stabilizing after a certain period, while higher fine aggregate content leads to a reduction in surface chloride concentration.
One issue with layer application of roller compacted concrete (RCC) is the development of cold joints, which can cause damage to RCC structures. In this study, fly ash was used in place of 0%, 20%, 40%, and 60% of the cement or aggregate to examine the impact of interlayer cold joint formation on RCC mixtures. To promote cold joint formation, the second layer was placed and compacted with a delay of 0, 60, 120, or 180 min after the first layer. Three methods were tried for preventing cold joints from forming: one was to apply a bedding mortar to the interlayer, another was to add a set retarder admixture, and the third was to spray an adhesion-enhancing chemical additive on the surface of the first layer. Based on the 28 d specimens’ compressive and splitting-tensile strengths as well as the depth of water penetration under pressure, the most effective method was found to be applying interlayer bedding mortar. Considering 180 min delayed layer castings, the splitting-tensile and compressive strengths of the control samples decreased by 31% and 17%, respectively, while the strengths of mixtures applying interlayer bedding mortar decreased by 9% and 10%. In addition, bedding mortar treatment decreased the water permeability by 59% compared to the control. Interlayer cold joint decreased all mixtures’ moduli of elasticity, regardless of the age of the specimens. When the interlayer delay was 60 min, the modulus of elasticity decreased by 1%–4%. It was between 2% and 14%, and between 10% and 24% at 120 and 180 min for the interlayer delay. The longer the delay in placing the second RCC layer, the more detrimental the effect of the cold joint. This effect was most noticeable on mechanical and permeability properties tested with applied load or water pressure parallel to the cold joint, such as flexural and splitting tensile strengths and water penetration depth under pressure.
To address the insufficient stiffness of the V-shaped reinforced concrete folded plate structure and its construction process causing environmental pollution, a novel assembled monolithic spherical-shaped reinforced concrete ribbed folded plate structure (AMRRFS) was proposed. The advantages of AMRRFS are that its construction process is environmentally friendly while it also exhibits great stability and rigidity. Therefore, an experimental and numerical investigation were conducted on the AMRRFS to investigate its mechanical properties. In addition, the parametric analysis of the AMRRFS was conducted, and some design recommendations were proposed. Under the design load, the experimental findings revealed that AMRRFS possessed excellent mechanical properties. During the overloading phase, the interface between the in situ casting area and the prefabrication area was severely damaged, leading to the loss of the structure’s ability to bear loads. The outcomes from the finite element simulations of AMRRFS closely mirrored the results of the experimental investigation. Based on the parametric analysis, it was recommended that the height of the AMRRFS, the height of the ribs, and the height of the secondary ridge beams shall be 1/7–1/5, 1/65–1/50, and 1/34–1/30 of the span, and that the minimum reinforcing ratio for all types of plates shall exceed 1.0%.
