A comprehensive framework for model validation and reliability assessment of container crane structures is pursued in this paper. The framework is composed of three phases. In phase I, a parameterized finite element model (FEM) of a typical type of container crane structure at Yangshan deep-water port, Shanghai, is developed for simulating the structure performance at controllable input/parameter settings. Full-scale experiments are conducted at multiple locations of the container crane for the validation assessment of the parameterized FEM. In phase II, high fidelity reliability model (HFRM) of the crane structure is constructed by incorporating the parameterized FEM, failure criteria, and uncertainty from multiple sources. To alleviate the computational burden for running finite element analysis at each function evaluation, surrogate modeling techniques, i.e., the polynomial response surface (PRS) and artificial neural networks (ANN) are applied to build lower fidelity reliability models (LFRM) using simulations generated from the high fidelity reliability model. Quantitative validation metrics, i.e., the area metric and u-pooling metric are applied to assess the degree to which the surrogates represent the high fidelity model in presence of uncertainty. Finally in phase III, the reliability of the container crane structure is assessed by estimating the probability of failure based on the surrogates of the high fidelity reliability model through structure reliability methods. The proposed framework provides a scientific and organized procedure for model validation activities, surrogate model building, and efficient reliability assessment to container cranes and other complex structures in engineering if necessary.
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Alyami H, Yang Z, Riahi R, Bonsall S, Wang J (2019) Advanced uncertainty modelling for container port risk analysis. Accid Anal Prev 123:411–421
Angus JE (1994) The probability integral transform and related results. SIAM Rev 36:652–654
ASME (2006) Guide for verification and validation in computational solid mechanics, V&V 10–2006. ASME, New York
Azeloglu O, Edinçliler A, Sagirli A (2014) Investigation of seismic behavior of container crane structures by shake table tests and mathematical modeling. Shock Vib 2014:1–9
Bayarri MJ, Berger JO, Rui P, Sacks J, Cafeo JA, Cavendish J, Lin C-H, Tu J (2007) A framework for validation of computer models. Technometrics 49:138–154
Bishop CM (1995) Neural networks for pattern recognition, Oxford University Press
Box GEP (2008) Response surfaces, mixtures and ridge analyses. J Am Stat Assoc 103:888–897
Bucher CG, Bourgund U (1990) A fast and efficient response surface approach for structural reliability problems. Struct Saf 7:57–66
Der Kiureghian A (1996) Structural reliability methods for seismic safety assessment: a review. Eng Struct 18:412–424
Der Kiureghian A, Dakessian T (1998) Multiple design points in first and second-order reliability. Struct Saf 20:37–49
Ditlevsen O, Madsen HO (1996) Structural reliability methods, 1st edn. Wiley, New York
Ender TR, Balestrini-Robinson S (2015) Surrogate modeling, modeling and simulation in the systems engineering life cycle: core concepts and accompanying lectures. Springer, London, pp 201–216
Engelund S, Rackwitz R (1993) A benchmark study on importance sampling techniques in structural reliability. Struct Saf 12:255–276
Evans D (1972) An application of numerical integration techniques to statistical tolerancing, II—A note on the error. Technometrics 13:315–324
Ferson S, Oberkampf WL (2009) Validation of imprecise probability models. Int J Reliab Saf 3:3–22
Ferson S, Oberkampf WL, Ginzburg L (2008) Model validation and predictive capability for the thermal challenge problem. Comput Methods Appl Mech Eng 197:2408–2430
Ghanem RG, Spanos PD (1991) Stochastic finite elements: a spectral approach. Springer, Berlin
Gunst RF, Myers RH, Montgomery DC (2016) Response surface methodology: process and product optimization using designed experiments, 4th edn. Wiley, New York
Higdon D, Kennedy M, Cavendish JC, Cafeo JA, Ryne RD (2004) Combining field data and computer simulations for calibration and prediction. SIAM J Sci Comput 26:448–466
Hills RG, Trucano TG (2001) Statistical validation of engineering and scientific models with application to CTH, Sandia National Laboratories
Jackson PS (1982) A second-order moments method for uncertainty analysis. IEEE Trans Reliab:382–384
Johnson NL, Kotz S, Balakrishna N (1994) Continuous univariate distributions, 2nd edn. Wiley-Interscience, New York
Kennedy MC, O'Hagan A (2001) Bayesian calibration of computer models. J R Stat Soc 63:425–464
Klopf A (1988) The drive-reinforcement neuronal model: a real-time learning mechanism for unsupervised learning, SPIE. Proceedings of the SPIE, United States, pp 119–121
Lee SH, Chen W (2009) A comparative study of uncertainty propagation methods for black-box-type problems. Struct Multidiscip Optim 37:239–253
Li W, Chen W, Jiang Z, Lu Z, Liu Y (2014) New validation metrics for models with multiple correlated responses. Reliability Engineering & System Safety 127:1–11
Li W, Chen S, Jiang Z, Apley DW, Lu Z, Chen W (2016) Integrating Bayesian calibration, bias correction, and machine learning for the 2014 Sandia Verification and Validation Challenge Problem, Journal of Verification. Validation and Uncertainty Quantification 1:1–18
Liu Y, Chen W, Arendt P, Huang H-Z (2011) Toward a better understanding of model validation metrics. J Mech Des 133:071005–071018
Madsen HO, Krenk S, Lind N (2006) Method of structural safety. Dover Publications, New York
Melchers RE (1989) Importance sampling in structural systems. Struct Saf 6:3–10
Montgomery DC (2012) Design and analysis of experiments, 9th edn. John Wiley & Sons, New York
More JJ (1977) The Levenberg- Marquardt algorithm: implementation and theory. Numerical Analysis:105–106
Murata N, Yoshizawa S, Amari SI (1994) Network information criterion-determining the number of hidden units for an artificial neural network model. IEEE Trans Neural Netw 5:865–872
Oberkampf WL, Barone MF (2006) Measures of agreement between computation and experiment: validation metrics. J Comput Phys 217:5–36
Oberkampf WL, Sindir M, Conlisk A (1998) Guide for the verification and validation of computational fluid dynamics simulations, American Institute of Aeronautics and Astronautics, AIAA
Oberkampf WL, Trucano TG, Hirsch C (2004) Verification, validation, and predictive capability in computational engineering and physics. Appl Mech Rev 57:345–384
Rahman S, Xu H (2004) A univariate dimension-reduction method for multi-dimensional integration in stochastic mechanics. Probabilistic Engineering Mechanics 19:393–408
Rajashekhar MR, Ellingwood BR (1993) A new look at the response surface approach for reliability analysis. Struct Saf 12:205–220
C.E. Rasmussen, Gaussian processes for machine learning, (2006)
Rebba R, Mahadevan S (2006) Validation of models with multivariate output. Reliability Engineering & System Safety 91:861–871
Rebba R, Huang S, Liu Y, Mahadevan S (2006) Statistical validation of simulation models. Int J Mater Prod Technol 25:164–181
Rodrigues ÉO (2018) Combining Minkowski and Cheyshev: new distance proposal and survey of distance metrics using k-nearest neighbours classifier. Pattern Recogn Lett 110:66–71
Sang HL, Kwak BM (2006) Response surface augmented moment method for efficient reliability analysis. Struct Saf 28:261–272
Seo HS, Kwak BM (2002) Efficient statistical tolerance analysis for general distribution using three-point information. Int J Prod Res 40:931–944
Soderberg E, Jordan M (2007) Seismic response of jumbo container cranes and design recommendations to limit damage and prevent collapse, American Society of Civil Engineers 11th triennial international conference on ports. San Diego, California, United States, pp 1–10
Sornette D, Davis A, Ide K, Vixie K, Pisarenko V, Kamm J (2007) Algorithm for model validation: theory and applications. Proc Natl Acad Sci 104:6562–6567
Xiu D, Karniadakis GE (2003) Modeling uncertainty in flow simulations via generalized polynomial chaos. J Comput Phys 187:137–167
Xu H, Rahman S (2010) A generalized dimension-reduction method for multidimensional integration in stochastic mechanics. Int J Numer Methods Eng 61:1992–2019
Zrnic N, Petković Z, Bošnjak S (2005) Automation of ship-to-shore container cranes: a review of state-of-the-art. FME Transactions 33:111–121
This work is supported by the National Natural Science Foundation of China (under Grant 51605279), the “Chenguang Program” supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission (No. 16CG54), and Opening Funding Supported by the Key Laboratory of Road Structure & Material Ministry of Transport (Research Institute of Highway Ministry of Transport).
Conflict of interest
The authors declare that they have no conflict of interest.
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Li, W., Quan, L., Hu, X. et al. A comprehensive framework for model validation and reliability assessment of container crane structures. Struct Multidisc Optim (2020). https://doi.org/10.1007/s00158-020-02637-w
- Structure reliability
- Model validation
- Model fidelity
- Container crane