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
Inverse problem-solving methods have found applications in various fields, such as structural mechanics, acoustics, and non-destructive testing. However, accurately solving inverse problems becomes challenging when observed data are incomplete. Fortunately, advancements in computer science have paved the way for data-based methods, enabling the discovery of nonlinear relationships within diverse data sets. In this paper, a step-by-step completion method of displacement information is introduced and a data-driven approach for predicting structural parameters is proposed. The accuracy of the proposed approach is 23.83% higher than that of the Genetic Algorithm, demonstrating the outstanding accuracy and efficiency of the data-driven approach. This work establishes a framework for solving mechanical inverse problems by leveraging a data-based method, and proposes a promising avenue for extending the application of the data-driven approach to structural health monitoring.
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 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.
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 (ρd) and molding water content (wini); 2) the ratio between T2 and pore radius, defined as the pore structure coefficient (Cr) of NMR, varies with pore water morphologies and soil types; 3) three methods were proposed to determine the value of Cr and can help to provide insights for better understanding of the NMR results in soil science.
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.
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.
The underground structure with stiffness mutation at the culvert-frame connection suffers from discrepant dynamic responses when earthquakes strike. This paper studies the seismic responses of an underground structure consisting of two portions of box frames and twin jacked culverts in soft soil. The two portions of box frames (Part A and Part C) have the same 2-storey and 3-span cross sections, rigidly connected to the two parallel jacked culverts (Part B) with F-type sockets between joints. A series of 1g shaking table tests is conducted on the modeled soil−structure system. Accelerometers are installed within the model soil, upon culverts, and on the top of mid slabs of box frames. Joint extensions and closures of culverts are measured. The white noise case is carried out to assess dynamic characteristics of the model system. Four synthetic earthquake cases are conducted to investigate the model system’s seismic responses under earthquakes with varying intensities. Ground acceleration responses of the model soil at different distances from Part A are compared. The extent of discrepancy in acceleration of culverts and box frames is quantified by the correlation coefficient. The characteristics of the joint extension and closure are summarized.
Ultra-high performance concrete (UHPC) has gained a lot of attention lately because of its remarkable properties, even if its high cost and high carbon emissions run counter to the current development trend. To lower the cost and carbon emissions of UHPC, this study develops a multi-objective optimization framework that combines the non-dominated sorting genetic algorithm and 6 different machine learning methods to handle this issue. The key features of UHPC are filtered using the recursive feature elimination approach, and Bayesian optimization and random grid search are employed to optimize the hyperparameters of the machine learning prediction model. The optimal mix ratios of UHPC are found by applying the multi-objective algorithm non-dominated sorting genetic algorithm-III and multi-objective evolutionary algorithm based on adaptive geometric estimation. The results are evaluated by technique for order preference by similarity to ideal solution and validated by experiments. The outcomes demonstrate that the compressive strength and slump flow of UHPC are correctly predicted by the machine learning models. The multi-objective optimization produces Pareto fronts, which illustrate the trade-off between the mix’s compressive strength, slump flow, cost, and environmental sustainability as well as the wide variety of possible solutions. The research contributes to the development of cost-effective and environmentally sustainable UHPC, and aids in robust, intelligent, and sustainable building practices.
