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 steel-shell concrete immersed tube (SSIT) with the self-compacting concrete (SCC) has been applied in the Shenzhen–Zhongshan Link, and the SSIT is prone to the void defect during the concrete pouring process. This work aims to study the flow behavior of the SCC and investigate the generation and distribution of the void defect in the SSIT, and the computational fluid dynamics (CFD) models are adopted to solve the above problems. To verify the CFD models, the slump test, L-box test, and field test based on Ok. the impact image method are carried out. The effects of the connecting hole spacing, the exhaust hole number, the exhaust hole position and the pouring speed on the flow behavior and the void defects distribution are quantitatively compared. According to the comparison results, the standard compartment with 300 mm connecting hole spacing and 10 exhaust holes is the optimal compartment structure design, and the concrete pouring speed of 15 m³/h is the optimal construction method. This work demonstrates that the CFD model offers a useful way to evaluate the generation and distribution features of the void defects for the steel–concrete–steel structure.

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.

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.

The computational modeling of fracture in solids using damage mechanics faces challenges with complex crack topology. This can be addressed by using a variational framework to reformulate the damage mechanics. In this paper, we propose several mathematically elegant variational damage models (VDMs) for fracture mechanics without explicitly using damage variables. Based on the energy density ϕ, the fracture energy density is formulated as \tilde{\varphi } = ϕ/(1 + ℓϕ/G_c) and the damage variable is expressed as s = ϕ/(ϕ + G_c/ℓ), which satisfy \tilde{\varphi }|_ϕ→∞ = G_c/ℓ and s|_ϕ→∞ = 1 as ϕ approaches infinity. These limits demonstrate that the new energy density converges to the Griffith energy release rate at full-damage state. The VDM profoundly modified the energy functional, implicitly incorporating the damage field. As a generalization of previous model, we propose a family of VDMs of varying orders. Additionally, we develop a multi-damage model to account for different types of energy densities, such as elastic thin plate and gradient elasticity. Using this functional, it is straightforward to deduce the governing equation for automatically evolving fractures. These formulations can be employed in conventional finite element method or other numerical methods with minimal modifications. Compared to the phase field method with the same mesh density, a sharper crack interface can be achieved. We demonstrate the capabilities of the proposed variational damage formulations using representative numerical examples.

Though nuclear magnetic resonance (NMR) has been applied in soil science over several decades, the quantitative relation between NMR signals and soil pore-water distribution is complex and still covered by some cloud. The major debates include: 1) the quantitative relation between transverse relaxation time (T₂) and pore radius varies in different studies; 2) Is the relation between NMR signals and soil–water contents unique? To clarify the aforementioned issues over the application of NMR in soil science, a comprehensive study was carried out. The results demonstrate that: 1) a unique linear relationship between peak area of NMR signals and soil volumetric water content (θ) exists, independent of the soil’s initial molding conditions, such as molding dry density (ρ_d) and molding water content (w_ini); 2) the ratio between T₂ and pore radius, defined as the pore structure coefficient (C_r) of NMR, varies with pore water morphologies and soil types; 3) three methods were proposed to determine the value of C_r and can help to provide insights for better understanding of the NMR results in soil science.

Stainless-steel provides substantial advantages for structural uses, though its upfront cost is notably high. Consequently, it’s vital to establish safe and economically viable design practices that enhance material utilization. Such development relies on a thorough understanding of the mechanical properties of structural components, particularly connections. This research advances the field by investigating the behavior of stainless-steel connections through the use of a four-parameter fitting technique and explainable artificial intelligence methods. Training was conducted on eight different machine learning algorithms, namely, Decision Tree, Random Forest, K-nearest neighbors, Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting, Adaptive Boosting, and Categorical Boosting. SHapley Additive Explanations was applied to interpret model predictions, highlighting features like spacing between bolts in tension and end-plate height as highly impactful on the initial rotational stiffness and plastic moment resistance. Results showed that Extreme Gradient Boosting achieved a coefficient of determination score of 0.99 for initial stiffness and plastic moment resistance, while Gradient Boosting model had similar performance with maximum moment resistance and ultimate rotation. A user-friendly graphical user interface (GUI) was also developed, allowing engineers to input parameters and get rapid moment–rotation predictions. This framework offers a data-driven, interpretable alternative to conventional methods, supporting future design recommendations for stainless-steel beam-to-column connections.

This paper mainly focuses on the establishment of an effective static estimation method for the extreme wind-induced force for clips between purlins and metal panels of the standing-seam metal roofing system (hereinafter referred to as SMRS) of typical double-slope light-weight steel portal frame structure considering dynamic characteristics of wind and structure. First, simultaneous pressure measurement with rigid gable roof models was conducted mainly considering the length-span ratio in the boundary layer wind tunnel of Tokyo Polytechnic University, Japan. Then, finite element modeling for SMRS according to the wind load path in the roofing system was carried out to check the actual wind load of the clips based on the traditional calculation method provided in design codes, and the spatial correlation of fluctuating wind pressure on the roof surface, as well as the dynamic effect of the roof structure itself, had been considered. According to the related Chinese, American, and Japanese codes, a magnification coefficient based on the traditional method of static wind-induced force for the clips was calculated and compared. Finally, a simplified estimation method of effective wind-induced force for the clips in typical zones on the roof surface considering dynamic characteristics was proposed.

This article presents an improved Elman neural network for reducing building vibrations during earthquakes. The adjustment coefficient is proposed to be added to the Elman network’s output layer to improve the controller’s performance when used to minimize vibrations in buildings. The parameters of the proposed Elman neural network model are optimized using the Balancing Composite Motion Optimization algorithm. The effectiveness of the proposed method is assessed using a three-story structure with an active dampening mechanism on the first level. The study also takes into account two kinds of Elman neural network input variables: displacement and velocity data on the first floor, as well as displacement and velocity readings across all three floors. This research uses two measures of fitness functions in the optimal process, the structure’s peak displacement and acceleration, to determine the best parameters for the proposed model. The effectiveness of the proposed method is demonstrated in restraining the vibration of the structure under a variety of earthquakes. Furthermore, the findings indicate that the proposed model maintains sustainability even when the maximum value of the actuator device is dropped.