Fatigue behaviour of composite timber-concrete beams

Refurbishment of existing buildings often claims for strengthening and stiffening of timber floors. To avoid too heavy interventions, this need is particularly relevant in seismic zones and/or for historical buildings, not only to preserve historical value, but also to contain the masses. A solution commonly adopted is to substitute the screed with a thin reinforced concrete or lightweight reinforced concrete slab duly connected to the timber beams, in such a way that a composite timber-concrete floor is obtained, granting also a sufficient rigidity in the horizontal plane. Moreover, this solution has also the advantage to improve the acoustic performance. Of course, the behavior of the composite structure depends on the rigidity of the shear connections. Since several type of shear connectors are available, the experimental assessment of its static and fatigue behavior is a prerequisite for a suitable design of the intervention. Aiming to compare their performances, an ad hoc experimental study has been carried out on three different types of shear connectors. The fatigue tests have been performed on a composite wood-concrete beam. During each test, 15000 loading-unloading cycles have been applied, recording the deformations and the relative slip. After completion of the load cycles, static load has been applied till to collapse. In the paper, the experimental tests and results are widely discussed, also in comparison with commonly used theoretical models, and relevant conclusions are draw

Extreme ground snow loads in Europe from 1951 to 2100

Lightweight roofs are extremely sensitive to extreme snow loads, as confirmed by recently occurring failures all over Europe. Obviously, the problem is further emphasized in warmer climatic areas, where low design values are generally foreseen for snow loads. Like other climatic actions, representative values of snow loads provided in structural codes are usually derived by means of suitable elaborations of extreme statistics, assuming climate stationarity over time. As climate change impacts are becoming more and more evident over time, that hypothesis is becoming controversial, so that suitable adaptation strategies aiming to define climate resilient design loads need to be implemented. In the paper, past and future trends of ground snow load in Europe are assessed for the period 1950–2100, starting from high-resolution climate simulations, recently issued by the CORDEX program. Maps of representative values of snow loads adopted for structural design, associated with an annual probability of exceedance p = 2%, are elaborated for Europe. Referring to the historical period, the obtained maps are critically compared with the current European maps based on observations. Factors of change maps, referred to subsequent time windows are presented considering RCP4.5 and RCP8.5 emission trajectories, corresponding to medium and maximum greenhouse gas concentration scenarios. Factors of change are thus evaluated considering suitably selected weather stations in Switzerland and Germany, for which high quality point measurements, sufficiently extended over time are available. Focusing on the investigated weather stations, the study demonstrates that climate models can appropriately reproduce historical trends and that a decrease of characteristic values of the snow loads is expected over time. However, it must be remarked that, if on one hand the mean value of the annual maxima tends to reduce, on the other hand, its standard deviation tends to increase, locally leading to an increase of the extreme values, which should be duly considered in the evaluation of structural reliability over time

Probabilistic methodology for the assessment of the impact of climate change on structural safety

Structural design is often governed by climatic actions, such as snow, wind, thermal and atmospheric icing loads, that will occur during the design service life. Since in structural standards climatic actions are usually derived from historical data series assuming stationary climate, alterations induced by climate change should be specifically evaluated, also to assess their influence on structural reliability. In the paper, a probabilistic methodology for the assessment of climate change impact on long-term structural reliability is presented, based on the analysis of observed data series and climate projections, provided by high resolution climate models. Factor of change uncertainty maps for climate extremes are derived starting from the analysis of weather series generated by an ad hoc weather generator, which considers homogenous populations of data suitably derived from climate model output. The long-term structural reliability is then assessed for reference structures at a given site considering the non-stationary nature of climatic actions by means of the pdfs of changes in extreme value parameters. Specifically, variations of the failure probability with time due to climate change are evaluated by moving time windows of forty years considering changes in mean load intensity and standard deviation of yearly maxima of the investigated climatic action. The results show the capability of the method to assess the impact of climate change on structural safety, highlighting the necessity of adaptation measure to maintain the required target reliability of the structure during its life

Reliability of roof structures subjected to snow loads

A proper evaluation of snow loads on roofs is crucial for structural design especially to guarantee an adequate reliability level of lightweight roof structures. The definition of roof snow load in structural codes is based on both the evaluation of ground snow loads and conversion factors from ground to roof load, which are function of the roof’s geometry, its exposure to wind and its thermal properties. However, reference values of roof snow loads are based only on an extreme value analysis carried out to derive characteristic values of ground snow load, while conversion factors are considered as deterministic quantities due to the lack of the data. In this paper, first a methodology to evaluate the reference value of roof snow load is presented based on the definition of probability density functions for ground snow loads and conversion factors accounting for roof’s geometry and its exposure to wind. The results lead to the definition of a design conversion factor which depend on the coefficient of variation of ground snow loads and are compared with the constant values provided by the Eurocode models, in EN1991-1-3:2003. Then, structural reliability is assessed for reference steel and timber structures located in different sites. Considering different proportions between variable and permanent loads, the reliability of flat roofs designed according to Eurocode provisions, provided by the current version and the new draft, is finally compared with the required target reliability levels

Stochastic identification of masonry parameters in 2D finite elements continuum models

The comprehension and structural modeling of masonry constructions is fundamental to safeguard the integrity of built cultural assets and intervene through adequate actions, especially in earthquake-prone regions. Despite the availability of several modeling strategies and modern computing power, modeling masonry remains a great challenge because of still demanding computational efforts, constraints in performing destructive or semidestructive in-situ tests, and material uncertainties. This paper investigates the shear behavior of masonry walls by applying a plane-stress FE continuum model with the Modified Masonry-like Material (MMLM). Epistemic uncertainty affecting input parameters of the MMLM is considered in a probabilistic framework. After appointing a suitable probability density function to input quantities according to prior engineering knowledge, uncertainties are propagated to outputs relying on gPCE-based surrogate models to considerably speed up the forward problemsolving. The sensitivity of the response to input parameters is evaluated through the computation of Sobol’ indices pointing out the parameters more worthy to be further investigated, when dealing with the seismic assessment of masonry buildings. Finally, masonry mechanical properties are calibrated in a probabilistic setting with the Bayesian approach to the inverse problem based on the available measurements obtained from the experimental loaddisplacement curves provided by shear compression in-situ tests

GPCE-based stochastic inverse methods: A benchmark study from a civil engineer’s perspective

In civil and mechanical engineering, Bayesian inverse methods may serve to calibrate the uncertain input parameters of a structural model given the measurements of the outputs. Through such a Bayesian framework, a probabilistic description of parameters to be calibrated can be obtained; this approach is more informative than a deterministic local minimum point derived from a classical optimization problem. In addition, building a response surface surrogate model could allow one to overcome computational difficulties. Here, the general polynomial chaos expansion (gPCE) theory is adopted with this objective in mind. Owing to the fact that the ability of these methods to identify uncertain inputs depends on several factors linked to the model under investigation, as well as the experiment carried out, the understanding of results is not univocal, often leading to doubtful conclusions. In this paper, the performances and the limitations of three gPCE-based stochastic inverse methods are compared: the Markov Chain Monte Carlo (MCMC), the polynomial chaos expansion-based Kalman Filter (PCE-KF) and a method based on the minimum mean square error (MMSE). Each method is tested on a benchmark comprised of seven models: four analytical abstract models, a one-dimensional static model, a one-dimensional dynamic model and a finite element (FE) model. The benchmark allows the exploration of relevant aspects of problems usually encountered in civil, bridge and infrastructure engineering, highlighting how the degree of non-linearity of the model, the magnitude of the prior uncertainties, the number of random variables characterizing the model, the information content of measurements and the measurement error affect the performance of Bayesian updating. The intention of this paper is to highlight the capabilities and limitations of each method, as well as to promote their critical application to complex case studies in the wider field of smarter and more informed infrastructure systems