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16 results for “Truss”
Surrogate Modeling Benchmark - Truss model
<p>This dataset is related to the truss model benchmark case. A detailed description of the benchmark case can be found on the public online community website UQWorld: <a href="https://uqworld.org/t/benchmark-case-truss-model/" target="_blank" rel="noopener">https://uqworld.org/t/benchmark-case-truss-model/</a>.</p> <p>The experimental designs include datasets with 100, 200, 300, 400, and 500 samples, each generated using optimized maximin distance Latin Hypercube Sampling (LHS) with 1000 iterations. Each dataset is replicated 20 times. The validation set contains 100,000 samples generated by Monte Carlo simulation. Each dataset contains input samples and the corresponding computational model responses.</p> <h2>Description of the dataset file</h2> <p>The dataset file includes two variables:</p> <ul> <li><em>ExpDesigns</em>, and</li> <li><em>ValidationSet</em>.</li> </ul> <p>Both variables are Matlab structures with fields <em>X</em>, <em>Y</em>, and <em>nSamples</em>. Variable <em>ExpDesigns</em> is a non-scalar structure sized according to the number of experimental design groups. Each field of <em>X</em> for the i-th element of the struct array contains replicated datasets, forming a matrix of size [number of samples] x [dimensionality] x [number of replications]. Similarly, each field of Y for the i-th element contains replicated computational model responses that correspond to the experimental design of the same replication, sized [number of samples] x [number of model outputs] x [number of replications]. The same structure logic applies to the <em>ValidationSet</em> variable, except it contains only one dataset per benchmark case.</p> <p>The structure can be summarized as follows:</p> <ul> <li>ExpDesigns(i).X(j,k,l) <ul> <li>i: dataset group,</li> <li>j: sample index,</li> <li>k: variable index, and</li> <li>l: replication index.</li> </ul> </li> </ul> <ul> <li>ExpDesigns(i).Y(j,m,l) <ul> <li>i, j, l: same as above,</li> <li>m: computational model output index.</li> </ul> </li> </ul> <ul> <li>ValidationSet.X(j,k) <ul> <li>j, k: same as above.</li> </ul> </li> </ul> <ul> <li>ValidationSet.Y(j,m) <ul> <li>j, m: same as above.</li> </ul> </li> </ul> <h2>Description of benchmarked metamodel competitors</h2> <p>The selection of competitors was based on our experience with meta-modeling and includes various metamodel types: Polynomial Chaos Expansions (PCE), Polynomial Chaos Kriging (PCK), and Kriging. Given that each metamodel has many hyperparameters, we chose the most general settings to address different benchmark case difficulties, including dimensionality, nonlinearity, and non-monotonicity.</p> <p>For <strong>Polynomial Chaos Expansions (PCE)</strong>, we used a polynomial degree and q-norm adaptivity approach. This approach adaptively increases the maximum polynomial degree and truncation q-norm until the estimated leave-one-out error starts increasing. Maximum polynomial interaction terms were limited to 2 due to the memory requirements for large model dimensionality and large experimental designs. We tested three different solvers to calculate the PCE coefficients: Least Angle Regression (LARS), Orthogonal Matching Pursuit (OMP), and Subspace Pursuit (SP).</p> <p><strong>Polynomial Chaos Kriging (PCK)</strong> employs a sequential combination strategy of PCE and Kriging. PCE uses degree adaptivity with a fixed q-norm. The maximum number of interactions is again set to 2 with the LARS solver. Ordinary Kriging is applied using the Matérn-5/2 correlation family, ellipsoidal, and anisotropic correlation function. We used a hybrid genetic algorithm to optimize the hyperparameters.</p> <p>We benchmarked both linear and ordinary <strong>Kriging</strong>, including Matérn-5/2 and Gaussian correlation families and separable and ellipsoidal correlation, resulting in eight different Kriging competitors. The hyperparameters were calculated using a hybrid covariance matrix adaptation-evolution strategy optimization.</p> <p>For further details on the settings, please refer to the competitors.m file and UQLab user manuals:</p> <ul> <li>S. Marelli, N. Luethen, B. Sudret, <a href="https://www.uqlab.com/pce-user-manual">UQLab User Manual – Polynomial Chaos Expansions</a>, Report UQLab-V2.1-104, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2024.</li> <li>C. Lataniotis, D. Wicaksono, S. Marelli, B. Sudret, <a href="https://www.uqlab.com/kriging-user-manual">UQLab User Manual – Kriging (Gaussian Process Modeling)</a>, Report UQLab-V2.1-105, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2024.</li> <li>R. Schoebi, S. Marelli, B. Sudret, <a href="https://www.uqlab.com/pck-user-manual">UQLab User Manual – Polynomial Chaos Kriging</a>, Report UQLab-V2.0-109, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2022.</li> </ul> <h2>Description of the results file</h2> <p>The results file contains one variable: <em>Metrics</em>. It is a Matlab structure with fields corresponding to each competitor (currently 12). Each competitor field contains data of type non-scalar struct array. The performance metrics included are RelMSE, RelRMSE, RelMAE, MAPE, Q2, and RelCVErr. Each field of Metrics.(CompetitorName) for the i-th element of the struct array contains metrics corresponding to the replicated dataset and the competitor, structured as follows:</p> <ul> <li>Metrics.(CompetitorName)(i).(MetricName)(l)<br> <ul> <li>i: dataset group,</li> <li>l: replication index.</li> </ul> </li> </ul> <p>The description of the performance measures (metrics) can be found here: <a href="https://uqworld.org/t/metamodel-performance-measures/" target="_blank" rel="noopener">https://uqworld.org/t/metamodel-performance-measures/</a>.</p> <h2>Additional files</h2> <p>We provide files in three languages (MATLAB, Python, and Julia) to showcase how to work with datasets, results, and their visualization. The files are called <em>working_with_datafiles.*</em> (the extension depends on the selected language).</p> <h2>Acknowledgment</h2> <p>This project was supported by the Open Research Data Program of the ETH Board under Grant number EPFL SCR0902285. The calculations were run on the Euler cluster of ETH Zürich using the MATLAB-based UQLab software developed at the Chair of Risk, Safety and Uncertainty Quantification of ETH Zürich.</p>
Space-Fractional Truss Element - shape functions and stiffness matrix
<p>The following research data zipped to a .zip file is included.</p> <p>Data refers to formulas for shape functions (\(N_{i}\)) and stiffness matrix components (\(K_{ij}\)) for a non-local truss element formulated in terms of fractional calculus (space-Fractional Truss Element) and the finite element method.</p> <p>The data are described as follows:</p> <ul> <li>The .zip file contains information in the name about the number of nodes of the truss element (m).</li> <li>Formulas for shape functions written in different formats: <ul> <li>Display_Ni.svg shows the formula in a conveniently viewable form;</li> <li>LaTeX_Ni.txt contains a formula written in LaTeX syntax;</li> <li>Sym_Ni.txt contains a formula written in symbolic form, allowing loading into a calculation program (e.g., by loading a file and using the sympify() function);</li> <li>Plot_Ni.svg contains plotted shape functions.</li> </ul> </li> <li>Formulas for the elements of the stiffness matrix written in different formats:<br> <ul> <li>The formula for the elements of the stiffness matrix is as follows, <br>\(K_{ij}=EA\frac{L}{2}\int_{-1}^{1} B_{i}B_{j} d\xi. \)</li> <li>In the files, the formulas for \(B_{i} \) and the product of \(B_{i}B_{j} \) are written - in different formats, with naming analogous to that described for the shape functions.</li> </ul> </li> </ul>
Galindo-Carvajal Optimized Truss
<p>Structural Analysis of Optimized Truss</p>
Dataset for Reliability assessment of steel truss railway bridges at ultimate limit state under combined loads
<p>Dataset for Reliability assessment of steel truss railway bridges at ultimate limit state under combined loads (under review at Reliability Engineering & System Safety) by Mohammad Shihabuddin Khan, Colin Caprani, Roman Lenner, and Miroslav Sýkora.</p>
Database of homogenized behavior of octet truss structure
<p>The following database is a set of training and test data of macroscopic behavior of octet truss structure used to train an LSTM neural network</p>
Multi-objective optimization data (large scale truss)
<p>The file contains data obtained from the multi-objective optimization of a large scale truss</p>
Data sheet space truss
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Figure 1 from: Colihueque N, Corrales O, Yáñez M (2017) Morphological analysis of Trichomycterus areolatus Valenciennes, 1846 from southern Chilean rivers using a truss-based system (Siluriformes, Trichomycteridae). ZooKeys 695: 135-152. https://doi.org/10.3897/zookeys.695.13360
Figure 1 - Location of sample sites of Trichomycterus areolatus populations located in two river basins from southern Chile. Bueno River basin: A Tijeral River, and B Huilma River; Biobío River basin: C Biobío River.
Figure 4 from: Colihueque N, Corrales O, Yáñez M (2017) Morphological analysis of Trichomycterus areolatus Valenciennes, 1846 from southern Chilean rivers using a truss-based system (Siluriformes, Trichomycteridae). ZooKeys 695: 135-152. https://doi.org/10.3897/zookeys.695.13360
Figure 4 - Scatterplot for individual scores from Discriminant Function Analysis (DF1 on DF2) of three Trichomycterus areolatus populations from southern Chile according to 17 truss measurements derived from a truss network. Crosses indicate group centroids.
Figure 3 from: Colihueque N, Corrales O, Yáñez M (2017) Morphological analysis of Trichomycterus areolatus Valenciennes, 1846 from southern Chilean rivers using a truss-based system (Siluriformes, Trichomycteridae). ZooKeys 695: 135-152. https://doi.org/10.3897/zookeys.695.13360
Figure 3 - Scatterplot for individual scores from Principal Component Analysis (PC1 on PC2) of three Trichomycterus areolatus populations from southern Chile according to 22 truss measurements derived from a truss network.
Figure 2 from: Colihueque N, Corrales O, Yáñez M (2017) Morphological analysis of Trichomycterus areolatus Valenciennes, 1846 from southern Chilean rivers using a truss-based system (Siluriformes, Trichomycteridae). ZooKeys 695: 135-152. https://doi.org/10.3897/zookeys.695.13360
Figure 2 - Position of the anatomical landmarks used to measure the size of 22 morphological characters on Trichomycterus areolatus based on a truss network. Definition of each character and its classification according to body shape dimension covered by them was as follows: a Head length, 1–2 = ventral tip of the operculum to tip of the head, 2–4 = tip of the head to posterior margin of the head b Head depth, 1–4 = ventral tip of the operculum to posterior margin of the head, 3–4 = base of the pectoral fin to posterior margin of the head c Anterior body length, 1–3 = ventral tip of the operculum to base of the pectoral fin, 3–5 = prepelvic length; 4–5 = posterior margin of the head to base of the pelvic fin; 3–6 = base of the pectoral fin to anterior base of the dorsal fin; 4–6 = predorsal length d Middle body depth, 5–6 = base of the pelvic fin to anterior base of the dorsal fin, 5–8 = base of the pelvic fin to posterior base of the dorsal fin, 6–7 = anterior base of the dorsal fin to anterior base of the anal fin, 7–8 = anterior base of the anal fin to posterior base of the dorsal fin e Middle body length, 5–7 = base of the pelvic fin to anterior base of the anal fin, 6–8 = dorsal fin base length, 7–9 = anal fin base length f Posterior body length, 7–10 = anterior base of the anal fin to dorsal posterior margin of the caudal peduncle, 9–10 = posterior base of the anal fin to dorsal posterior margin of the caudal peduncle g Peduncle depth, 8–9 = anterior caudal peduncle depth, 10–11 = posterior caudal peduncle depth h Peduncle length, 8–10 = dorsal caudal peduncle length, 9–11 = ventral caudal peduncle length.
Topology generation and quantitative stiffness analysis for fiber networks based on disordered spatial truss
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Abscisic acid regulates plant growth and flowering through SINGLE FLOWER TRUSS pathway in tomato.
GEO Series GSE45243. Solanum pimpinellifolium. 4 samples. Type: Expression profiling by high throughput sequencing.
SINGLE FLOWER TRUSS and SELF-PRUNING differentially regulate developmental and metabolic genetic networks to guide cotton (Gossypium hirsutum) architectures.
GEO Series GSE144546. Gossypium hirsutum. 54 samples. Type: Expression profiling by high throughput sequencing.
SINGLE FLOWER TRUSS and SELF-PRUNING differentially regulate developmental and metabolic genetic networks to guide cotton (Gossypium hirsutum) architectures [GEO_SFT_SP]
GEO Series GSE144545. Gossypium hirsutum. 18 samples. Type: Expression profiling by high throughput sequencing.
SINGLE FLOWER TRUSS and SELF-PRUNING differentially regulate developmental and metabolic genetic networks to guide cotton (Gossypium hirsutum) architectures [meristems]
GEO Series GSE144544. Gossypium hirsutum. 36 samples. Type: Expression profiling by high throughput sequencing.
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