Review of strength models for masonry spandrels

Many older unreinforced masonry (URM) buildings feature timber floors and solid brick masonry. Simple equivalent frame models can help predicting the expected failure mechanism and estimating the strength of a URM wall. When modelling a URM wall with an equivalent frame model rather than, for example, a more detailed simplified micro-model, the strengths of the piers and spandrels need to be estimated from mechanical or empirical models. Such models are readily available for URM piers, which have been tested in many different configurations. On the contrary, only few models for spandrel strength have been developed. This paper reviews these models, discusses their merits, faults and compares the predicted strength values to the results of recent experimental tests on masonry spandrels. Based on this assessment, the paper outlines recommendations for a new set of strength equations for masonry spandrel

Seismic fragility assessment of bridges with as-built and retrofitted splice-deficient columns

A significant proportion of existing bridges in high seismic regions were constructed prior to the 1970s. As a result of poor reinforcement detailing, pre-1970s bridge columns are susceptible to lap-splice or shear failure in the plastic region. Given the high economic impact of retrofitting all pre-1970s reinforced concrete (RC) bridges, it is essential to identify the most vulnerable bridges for retrofit prioritisation. Analytical fragility functions are useful for quantifying the seismic vulnerability of existing bridge stock. However, the accuracy of these fragility functions relies on the adequacy of the adopted modelling approach. This paper presents a hinge-type modelling approach for capturing the seismic response of as-built splice-deficient and retrofitted RC bridge columns. Fragility analysis is carried out for typical seat and diaphragm abutment two-span bridges using the proposed hinge-type modelling approach. The results showed that the vulnerability of the bridges depends on the column failure mode and the limit state under consideration. Also, the common notion that the column is the most vulnerable component may not necessarily be true. The study underscored that retrofitting columns without retrofitting other components may not effectively mitigate the damage and associated risk

Numerical study on the force-deformation behaviour of masonry spandrels with arches

This paper presents a numerical study on the force-deformation behaviour of masonry spandrels supported on arches which are analysed using simplified micro models. The model is validated against results from quasi-static cyclic tests on masonry spandrels. A large range of spandrels with different arch geometries, material properties and axial load ratios are studied. The numerical results are compared to peak strength values predicted with an existing mechanical model. Finally, estimates for the initial stiffness and the spandrel rotation associated with the onset of strength degradation are derived

Predicting the dissolution kinetics of silicate glasses using machine learning

Predicting the dissolution rates of silicate glasses in aqueous conditions is a complex task as the underlying mechanism(s) remain poorly understood and the dissolution kinetics can depend on a large number of intrinsic and extrinsic factors. Here, we assess the potential of data-driven models based on machine learning to predict the dissolution rates of various aluminosilicate glasses exposed to a wide range of solution pH values, from acidic to caustic conditions. Four classes of machine learning methods are investigated, namely, linear regression, support vector machine regression, random forest, and artificial neural network. We observe that, although linear methods all fail to describe the dissolution kinetics, the artificial neural network approach offers excellent predictions, thanks to its inherent ability to handle non-linear data. Overall, we suggest that a more extensive use of machine learning approaches could significantly accelerate the design of novel glasses with tailored properties

Review of strength models for masonry spandrels

Many older unreinforced masonry (URM) buildings feature timber floors and solid brick masonry. Simple equivalent frame models can help predicting the expected failure mechanism and estimating the strength of a URM wall. When modelling a URM wall with an equivalent frame model rather than, for example, a more detailed simplified micro-model, the strengths of the piers and spandrels need to be estimated from mechanical or empirical models. Such models are readily available for URM piers, which have been tested in many different configurations. On the contrary, only few models for spandrel strength have been developed. This paper reviews these models, discusses their merits, faults and compares the predicted strength values to the results of recent experimental tests on masonry spandrels. Based on this assessment, the paper outlines recommendations for a new set of strength equations for masonry spandrels

Stochastic response of reinforced concrete buildings using high dimensional model representation

Dynamic responses of structures are random in nature due to the uncertainties in geometry, material properties, and loading. The random dynamic responses can be represented fairly well by stochastic analysis. The methods used for stochastic analysis can be grouped into statistical and non-statistical approaches. Although statistical approaches like Monte Carlo simulation is considered as an accurate method for the stochastic analysis, computationally less intensive yet efficient, simplified non-statistical methods are necessary as an alternative. The present study is an evaluation of a relatively new non-statistical metamodel-based approach known as, High Dimensional Model Representation, with reference to existing response surface methods such as Central Composite Design, Box Behnken Design, and Full Factorial Design, in a dynamic response analysis. The geometry of a reinforced concrete frame is chosen to conduct free vibration and nonlinear dynamic analysis to study the stochastic responses using High Dimensional Model Representation method. This method was found to provide results as good as other methods with less computational effort with regard to the selected case studies