•A modified consistent imperfection method is proposed for buckling analysis.•A prediction method is proposed using machine learning to predict the buckling load.•A simplified formula based on SVR ...with linear kernel is given for calculating the buckling load.
A modified method is proposed for the consistent imperfection method, which introduces the shape of the first-order linear buckling mode of a spatial structure as the imperfection pattern to estimate the non-linear buckling load of an imperfect structure. By adjusting the magnitudes of the joint deflection component and the member deformation component, the imperfection pattern calculated by the modified method is shown to be more reasonable to represent the construction and fabrication errors in reality. It is also found that the imperfection pattern calculated by the first-order linear buckling mode may not be the most adverse one to the structure, and the non-linear buckling load of the imperfect structure is highly associated with the similarity between the imperfection pattern and the deformation of the structure, which is described by the Euclidean distance between the imperfection pattern and the incremental displacement vectors. Subsequently, a prediction method of the non-linear buckling load of an imperfect structure is proposed using machine learning techniques, including the artificial neural network and the support vector regression (SVR), which avoid the computation cost of multiple non-linear buckling analysis for imperfect structures. Finally, the performance of the prediction method is verified, and a simplified formula is given for the design of reticulated shells based on the SVR with the linear kernel.
•Joint gap index is proposed for describing assembling convenience of aluminum alloy structures;•A form-finding method is proposed for aluminum alloy reticulated structures.•The proposed method is ...effective in providing a wide set of solutions with trade-off relations for engineers to choose.•Actual joint rigidity should always be considered since it influences the form-finding result.
This paper proposes a form-finding method of free-form aluminum alloy reticulated structures with semi-rigid gusset joints. The vertical coordinates of the control points of the B-spline surface are selected to be the design variables adjusting the shape of the structure. The joint gap index, which is proposed for characterizing the manufacturing and assembling convenience of aluminum alloy structures, the strain energy, and the safety factor under short-term snow load, are chosen as the objective functions of the multi-objective optimization problem. The Pareto optimal solutions of the numerical example indicate that slight differences in the structural shape can result in quite different mechanical behavior, and the NSGA-II algorithm is effective in providing a wide range of optimal structures as compromise solutions of the multi-objective problem. Finally, the same optimization problem is solved for rigid jointed structures to study the influence of the joint rigidity on the form-finding result. By comparison of the internal force distribution, it is found that the joint semi-rigidity will influence the optimal structural shape due to the difference in the load-bearing mechanism. Therefore, it is suggested that the actual joint rigidity should be considered in the structural optimization process of reticulated structures.
Apocynum venetum L. (Apocynaceae) is valuable for its medicinal compounds and fiber content. Native A. venetum populations are threatened and require protection. Wild A. venetum resources are limited ...relative to market demand and a poor understanding of the composition of A. venetum at the molecular level. The chloroplast genome contains genetic markers for phylogenetic analysis, genetic diversity evaluation, and molecular identification. In this study, the entire genome of the A. venetum chloroplast was sequenced and analyzed. The A. venetum cp genome is 150,878 bp, with a pair of inverted repeat regions (IRA and IRB). Each inverted repeat region is 25,810 bp, which consist of large (LSC, 81,951 bp) and small (SSC, 17,307 bp) single copy areas. The genome-wide GC content was 38.35%, LSC made up 36.49%, SSC made up 32.41%, and IR made up 43.3%. The A. venetum chloroplast genome encodes 131 genes, including 86 protein-coding genes, eight ribosomal RNA genes, and 37 transfer RNA genes. This study identified the unique characteristics of the A. venetum chloroplast genome, which will help formulate effective conservation and management strategies as well as molecular identification approaches for this important medicinal plant.
The eccentric RHS (rectangular hollow sections) joint offers improved mechanical properties and better space utilization. Its use in frame structures has gained significant attention. Currently, the ...initial rotational stiffness of RHS joints, the simplified finite element analysis method of eccentric RHS joints, and the influence of the spatial effect of RHS joints are still unknown. The purpose of this research is to establish a calculation formula for the initial rotational stiffness of eccentric RHS joints, study the influence of the spatial effect under complex stress conditions, and propose a mathematical model that can be used to simplify the analysis of eccentric RHS joints. The research findings indicate that the web plate's deformation stiffness primarily influences the joints' initial rotational stiffness. This increases with a higher beam-to-column depth-to-width ratio, beam-to-column thickness ratio, and column width-to-thickness ratio. The form of the moment distribution in the joint changes, and begins to have a significant effect on the rotational stiffness when the beam-to-column flange width ratio reaches and exceeds 0.7. The stiffeners have a direct additive effect on the joint stiffness. The influence of adjacent beams on the joint is minimal, and the spatial effect of the joint can be disregarded. Furthermore, the finite element analysis confirmed the accuracy of the power function model in accurately simulating the static load behavior of the joint, particularly the bending moment-angle relationship.
•Machine-specified ground structure is proposed for optimization of binary trusses.•Trained agent can generate various stable ground structures with a given node-set.•Various node-sets can be handled ...without re-training owing to graph embedding.•Machine-specified ground structures are more likely to obtain global optimum.
This paper proposes the concept of machine-specified ground structures for topology optimization of trusses. Unlike general ground structures with dense and regular connectivity, machine-specified ground structures are sparse stable ground structures with a specified number of members designed by machines. Firstly, the generation process of machine-specified ground structures from a given node-set is formulated as a reinforcement learning task. Graph embedding is used to integrate the structural information into a comprehensive feature matrix to describe the state. By establishing the policy network, the probability of each action, i.e., selecting each node in the node-set, is obtained based on the comprehensive feature matrix. The task is solved using a gradient-based algorithm called REINFORCE. A randomized 4 × 4 node-set is used to train the agent. The policy converges with a high average reward, and generates different yet reasonable structures because a stochastic policy is employed. Besides, the agent can handle different-sized node-sets without re-training. Hence, the machine-specified ground structures generated by the trained agent can be utilized to assist the structural topology design. Subsequently, a method for a typical problem with singular optimal solutions, i.e., topology optimization of binary trusses with stress and displacement constraints, is proposed based on machine-specified ground structures. Finally, through different-sized numerical examples, it is demonstrated that the machine-specified ground structures lead to a variety of optimal solutions, and it is more likely to obtain the global optimum than fully-connected ground structures. It is worth noting that machine-specified ground structures can also be applied to other problems without re-training.
Single-layer reticulated shells (SLRSs) find widespread application in the roofs of crucial public structures, such as gymnasiums and exhibition center. In this paper, a new neural-network-based ...method for structural damage identification in SLRSs is proposed. First, a damage vector index,
NDL
, that is related only to the damage localization, is proposed for SLRSs, and a damage data set is constructed from
NDL
data. On the basis of visualization of the
NDL
damage data set, the structural damaged region locations are identified using convolutional neural networks (CNNs). By cross-dividing the damaged region locations and using parallel CNNs for each regional location, the damaged region locations can be quickly and efficiently identified and the undamaged region locations can be eliminated. Second, a damage vector index, DS, that is related to the damage location and damage degree, is proposed for SLRSs. Based on the damaged region identified previously, a fully connected neural network (FCNN) is constructed to identify the location and damage degree of members. The effectiveness and reliability of the proposed method are verified by considering a numerical case of a spherical SLRS. The calculation results showed that the proposed method can quickly eliminate candidate locations of potential damaged region locations and precisely determine the location and damage degree of members.
This paper proposes a framework for critical element identification and demolition planning of frame structures. Innovative quantitative indices considering the severity of the ultimate collapse ...scenario are proposed using reinforcement learning and graph embedding. The action is defined as removing an element, and the state is described by integrating the joint and element features into a comprehensive feature vector for each element. By establishing the policy network, the agent outputs the Q value for each action after observing the state. Through numerical examples, it is confirmed that the trained agent can provide an accurate estimation of the Q values, and handle problems with different action spaces owing to utilization of graph embedding. Besides, different behaviors can be learned by varying hyperparameters in the reward function. By comparing the proposed method and the conventional sensitivity index-based methods, it is demonstrated that the computational cost is considerably reduced because the reinforcement learning model is trained offline. Besides, it is proved that the Q values produced by the reinforcement learning agent can make up for the deficiencies of existin g indices, and can be directly used as the quantitative index for the decision-making for determining the most expected collapse scenario, i.e., the sequence of element removals.
In this study, experimental research, a numerical simulation, and a theoretical analysis were performed on the bearing capacity of an aluminum alloy circular tube filled with a lightweight filler ...(ATLF). Bearing capacity tests were conducted for six ATLF columns and two aluminum alloy hollow tubes (AAHTs), and their local buckling failure modes and mechanical properties were obtained. A finite element model was developed using ABAQUS software (ABAQUS 2016, ABAQUS Inc., Palo Alto, CA, USA) for numerical calculations. Furthermore, a large-scale numerical analysis was performed to investigate the effect of structural parameters, such as the tube thickness, diameter, column length, and initial geometric imperfections, as well as the aluminum alloy’s properties and the lightweight filler’s properties, on the bearing capacity of the ATLF columns and AAHTs under axial compression. Based on the test and numerical analysis results, a formula for calculating the local buckling stress of AAHTs under axial compression is proposed. An improved coefficient of bearing capacity for the ATLF columns caused by the internal lightweight filler was obtained by fitting, and based on the results, a formula for computing the bearing capacity of ATLF columns under axial compression is proposed in this study.
Quinoa (
Willd.) is a highly nutritious food product with a comprehensive development prospect. Here, we discussed the effect of
11B91 on the growth, development and salt tolerance (salt ...concentrations: 0, 150, 300 mmol·L
) of quinoa and highlighted a positive role for the application of plant growth-promoting rhizobacteria bacteria in quinoa. In this artical, the growth-promoting effect of
11B91 on quinoa (Longli No.1) and the changes in biomass, chlorophyll content, root activity and total phosphorus content under salt stress were measured. The results revealed that plants inoculated with 11B91 exhibited increased maximum shoot fresh weight (73.95%), root fresh weight (75.36%), root dry weight (136%), chlorophyll
(65.32%) contents and chlorophyll
(58.5%) contents, root activity (54.44%) and total phosphorus content (16.66%). Additionally, plants inoculated with 11B91 under salt stress plants showed significantly improved, fresh weight (107%), dry weight (133%), chlorophyll
(162%) contents and chlorophyll
(76.37%) contents, root activity (33.07%), and total phosphorus content (42.73%).
In this study, a numerical analysis was conducted on aluminum alloy reticulated shells (AARSs) with gusset joints under fire conditions. First, a thermal-structural coupled analysis model of AARSs ...considering joint semi-rigidity was proposed and validated against room-temperature and fire tests. The proposed model can also be adopted to analyze the fire response of other reticulated structures with semi-rigid joints. Second, a parametric analysis was conducted based on the numerical model to explore the buckling behavior of K6 AARS with gusset joints under fire conditions. The results indicated that the span, height-to-span ratio, height of the supporting structure, and fire power influence the reduction factor of the buckling capacity of AARSs under fire conditions. In contrast, the reduction factor is independent of the number of element divisions, number of rings, span-to-thickness ratio, and support condition. Subsequently, practical design formulae for predicting the reduction factor of the buckling capacity of K6 AARSs were derived based on numerical analysis results and machine learning techniques to provide a rapid evaluation method. Finally, further numerical analyses were conducted to propose practical design suggestions, including the conditions of ignoring the ultimate bearing capacity analysis of K6 AARS and ignoring the radiative heat flux.
