Probabilistic design methods for steel structures using refined models of resistance and loads

Abstract. Anticipating risks related to climate extremes often relies on the quantification of large return levels (values exceeded with small probability) from climate projection ensembles. Current approaches based on multi-model ensembles (MMEs) usually estimate return levels separately for each climate simulation of the MME. In contrast, using MME obtained with different combinations of general circulation model (GCM) and regional climate model (RCM), our approach estimates return levels together from the past observations and all GCM–RCM pairs, considering both historical and future periods. The proposed methodology seeks to provide estimates of projected return levels accounting for the variability of individual GCM–RCM trajectories, with a robust quantification of uncertainties. To this aim, we introduce a flexible non-stationary generalized extreme value (GEV) distribution that includes (i) piecewise linear functions to model the changes in the three GEV parameters and (ii) adjustment coefficients for the location and scale parameters to adjust the GEV distributions of the GCM–RCM pairs with respect to the GEV distribution of the past observations. Our application focuses on snow load at 1500 m elevation for the 23 massifs of the French Alps. Annual maxima are available for 20 adjusted GCM–RCM pairs from the EURO-CORDEX experiment under the scenario Representative Concentration Pathway (RCP) 8.5. Our results show with a model-as-truth experiment that at least two linear pieces should be considered for the piecewise linear functions. We also show, with a split-sample experiment, that eight massifs should consider adjustment coefficients. These two experiments help us select the GEV parameterizations for each massif. Finally, using these selected parameterizations, we find that the 50-year return level of snow load is projected to decrease in all massifs by −2.9 kN m−2 (−50 %) on average between 1986–2005 and 2080–2099 at 1500 m elevation and RCP8.5. This paper extends the recent idea to constrain climate projection ensembles using past observations to climate extremes.

Rainfall intensity–duration–frequency relationships derived from large partial duration series

Anticipating risks related to climate extremes often relies on the quantification of large return levels (values exceeded with small probability) from climate projection ensembles. Current approaches based on multi-model ensembles (MMEs) usually estimate return levels separately for each chain of the MME. By contrast, using MME obtained with different combinations of general circulation model (GCM) and regional climate model (RCM), our approach estimates return levels together from the past observations and all GCM-RCM pairs, considering both historical and future periods. The proposed methodology seeks to provide estimates of projected return levels accounting for the variability of individual GCM-RCM trajectories, with a robust quantification of uncertainties. To this aim, we introduce a flexible non-stationary generalized extreme value (GEV) distribution that includes i) piecewise linear functions to model the changes in the three GEV parameters ii) adjustment coefficients for the location and scale parameters to adjust the GEV distributions of the GCM-RCM pairs with respect to the GEV distribution of the past observations. Our application focuses on snow load at 1500 m elevation for the 23 massifs of the French Alps, which is of major interest for the structural design of roofs. Annual maxima are available for 20 adjusted GCM-RCM pairs from the EURO-CORDEX experiment, under the scenario RCP8.5. Our results show with a model-as-truth experiment that at least two linear pieces should be considered for the piecewise linear functions. We also show, with a split-sample experiment, that eight massifs should consider adjustment coefficients. These two experiments help us select the GEV parameterizations for each massif. Finally, using these selected parameterizations, we find that the 50-year return level of snow load is projected to decrease in all massifs, by −2.9 kN m−2 (−50 %) on average between 1986–2005 and 2080–2099 at 1500 m elevation and RCP8.5. This paper extends to climate extremes the recent idea to constrain climate projection ensembles using past observations.

Sandwich panels composed of glass fiber–reinforced polymer (GFRP) skins and low-density polyurethane core are investigated for potential structural applications such as lightweight insulated roofing or cladding systems. Ten 1,220×318×78-mm panels, including three control specimens, were tested under high-cycle fatigue at maximum load levels of 45–70% of the ultimate static load. Fatigue tests were carried out to failure, thereby enabling the development of the complete fatigue life (S-N) curve and stiffness degradation. Digital imaging correlation was used to map shear deformations and showed that shear contributes about 90% of the deflection. Both static and fatigue failure modes were due to core shear failure. The study showed that fatigue threshold was 37% of the ultimate static capacity, and that to sustain at least 2 million cycles, service load should be limited to 45% of ultimate. The maximum design service live and snow loads, as well as wind load based on the most critical conditions in Canada, were all below the fatigue threshold of the panel. The panels had a strength safety factor of 2.1 relative to the factored maximum design live and snow load, and 6.5 relative to the factored maximum wind load. Deflection limits may govern for some service load conditions, but is not critical when only wind load considered. Stiffness degradation reached a maximum of 15–20%, but residual deflections upon unloading were insignificant.

The widespread use of vertical cylindrical tanks makes the question of their sustainable design. Snow load brings the greatest contribution to the stress-strain state of the supporting structures of spherical dome covering vertical tanks. Developed a new geometrical forms of shapes covering of the tank with volume 20 000 m3. There is no recommendations of wind and snow loads on developed forms in Russian and international codes. The experimental investigations of wind and snow loads acting on tanks dome roofs were performed in boundary layer wind tunnel. Aerodynamic coefficients of an external pressure and a velocity of a wind near the roof were defined. Spreading and a value of snow loads also were defined. The results of investigations shows that snow masses on the roofs of suggested forms placed more symmetrical then on the typical spherical dome coverings of the vertical tanks. With allowance for experimental results numerical analysis of dome roofs was performed. Comparative analysis showed that the roofs of suggested forms are more effective than typical spherical and conical dome roofs of vertical cylindrical tanks.

Reliability of steel roof structures under snow loads

A two-component generalized extreme value distribution for precipitation frequency analysis

The principle objective of this project is to analysis and design a multi-storeyed building [G + 10 (3-dimensional frame)] using SAP2000. The design involves load calculations manually and analyzing the whole structure by SAP2000. The design methods used in SAP2000 analysis are conforming to Indian Standard Code of Practice and features a state-of-the-art user inter face, visualization tools, powerful analysis and design engines with advanced finite element and dynamic analysis capabilities. From model generation, analysis and design to visualization and result verification. The project is designed in accordance with Indian codes, considering various loads such as Reinforced concrete (IS 456-2000), dead load (IS875-1987 part1), live load (IS875-1987 part2), wind load (IS875-1987 part3), seismic load (IS1893-2016 part1) and load combinations are considered as per the (IS875-1987 part-5) code books. The analysis considers seismic zone III and ensures safety by considering dead load, live load, seismic and wind forces, and balancing economy and safety. Structural design is crucial for Civil Engineers, and proper analysis can prevent structure failure and loss of life. Engineers analyze structures considering constraints like serviceability and deformability, following IS codes. Our project focuses on analyzing and designing superstructures like slabs, beams, and columns, considering dead load, live load, seismic and wind forces Response Spectrum Analysis and Shear Wall. The analysis parameters such as shear force, bending moment, and displacement. KEYWORDS: Response Spectrum Analysis, Shear Wall, SAP2000, Seismic and Wind loads.

In order to solve the problem of transmission line galloping breakage under ice storm, a probability prediction model of transmission line breaking based on improved particle swarm optimization is proposed. Firstly, the generalized extreme value (GEV) distribution parameters of wind load and ice load of transmission lines under ice storm disasters are determined by improved particle swarm optimization (PSO) algorithm, then the excess distribution of the ice load and wind load of the transmission line is obtained according to the critical wind speed and critical icing density as the transmission line begins to gallop. And then, based on the binary t-Copula function, the joint probability distribution of ice load and wind load is obtained, and breaking probability of transmission line galloping under ice storm disaster is realized. Finally, using actual historical data of transmission line breaking in winter in Hunan Province, the superiority of the improved PSO algorithm and the accuracy of the proposed prediction model are verified, which can lay a foundation for transmission line galloping breakage prediction and provide a basis for pre-deployment of anti-galloping measures.

Modelling the risk of snow damage to forests under short-term snow loading

This study developed a snow load alert system, known as the “YukioroSignal”; this system aims to provide a widespread area for assessing snow load distribution and the information necessary for aiding house roof snow removal decisions in snowy areas of Japan. The system was released in January 2018 in Niigata Prefecture, Japan, and later, it was expanded to Yamagata and Toyama prefectures in January 2019. The YukioroSignal contains two elements: the “Quasi-Real-Time Snow Depth Monitoring System,” which collects snow depth data, and the numerical model known as SNOWPACK, which can calculate the snow water equivalent (SWE). The snow load per unit area is estimated to be equivalent to SWE. Based on the house damage risk level, snow load distribution was indicated by colors following the ISO 22324. The system can also calculate post-snow removal snow loads. The calculated snow load was validated by using the data collected through snow pillows. The simulated snow load had a root mean square error (RMSE) of 21.3%, which was relative to the observed snow load. With regard to residential areas during the snow accumulation period, the RMSE was 13.2%. YukioroSignal received more than 56,000 pageviews in the snowheavy 2018 period and 26,000 pageviews in the less snow-heavy 2019 period.

Strands produced from small-diameter timbers of lodgepole and ponderosa pine were used to fabricate a composite sandwich structure as a replacement for traditional building envelope materials, such as roofing. It is beneficial to develop models that are verified to predict the behavior of these sandwich structures under typical service loads. When used for building envelopes, these structural panels are subjected to bending due to wind, snow, live, and dead loads during their service life. The objective of this study was to develop a theoretical and a finite element (FE) model to evaluate the elastic bending behavior of the wood-strand composite sandwich panel with a biaxial corrugated core. The effect of shear deformation was shown to be negligible by applying two theoretical models, the Euler–Bernoulli and Timoshenko beam theories. Tensile tests were conducted to obtain the material properties as inputs into the models. Predicted bending stiffness of the sandwich panels using Euler-Bernoulli, Timoshenko, and FE models differed from the experimental results by 3.6%, 5.2%, and 6.5%, respectively. Using FE and theoretical models, a sensitivity analysis was conducted to explore the effect of change in bending stiffness due to intrinsic variation in material properties of the wood composite material.

The generalized extreme value (GEV) distribution is suitable for modeling the extreme events, such as the maximum value of an asymptotic distribution. This paper presents results of an experimental investigation where the distribution of peak-to-average power ratio (PAPR) in downlink MC-CDMA systems is modeled by the GEV distribution. Two orthogonal sets of sequences, Walsh-Hadamard and Golay complementary sequences, are used in spreading processes in the system. Then the parameters of the GEV distribution are estimated for the PAPR distribution. Through intensive numerical results, it is shown that the GEV distribution is an accurate model of the PAPR distribution of MC-CDMA systems. Also, the statistically estimated GEV distribution parameters for the PAPR reveal that when the number of subcarriers increases, the PAPR distributions converge to the Gumbel distribution.

At present, there are mainly three research ways on the distribution of snow load: field measurement, experimental simulation, and numerical simulation. They all need expensive equipment, specimen model used only once, and slight defective simulation software. Artificial Neural Networks (ANNs) rely on a large number of input functions to estimate unknown or known occurrences. This paper applies ANNs to predict the uneven distribution of snow loads. First, the snow load database is collected and set up, including all the data from field measurement and experimental simulation. Second, part of the data from the 1 m3 model is used as a training set to train Backpropagation Neural Networks (BPNN) and Generalized Regression Neural Networks (GRNN). The parameters of the two neural networks are determined (such as the smoothing factor in the GRNN), and the rest of the data is used to examine the model; the prediction and measurement results of the two models are compared. Finally, based on different wind direction angles and wind speed, a GRNN model is established to predict the uneven snow load distribution. Through comparative analysis, GRNN is more suitable for predicting the uneven distribution of snow loads, especially in predicting snow thickness. In particular, the prediction effect of the model on the snow load distribution trend is good, and the prediction ability of the neural network model is displayed.

Snow loads for structural design are calculated as the product of the ground snow load and a snow load coefficient that transforms the ground load to a roof load. This paper presents a statistical analysis of annual extreme water‐equivalents (reported as inches of water) of ground snow measured at 76 weather stations in the northeast quadrant of the United States through the winter of 1979–1980. The analysis suggests that the lognormal distribution is preferable to a Type I distribution of extreme values for describing annual extreme ground snow loads at a majority of sites. Sampling errors and the selection of design loads from the statistical analysis are also described.