Civil Engineering

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Investigating uncertainties in shear resistance prediction of beams without stirrups.
(2023) David, Abayomi Bolarinwa.; Olalusi, Oladimeji Benedict.
Optimizing a model’s performance should keep the functionality within the confines of safety and economy; deviation from these calls for a reliability investigation of such models. In a bit to optimize shear models for simplicity, safety functionality and economic performance have been an issue of a trade-off as the overestimation or underestimation of the model's intended purpose may occur. Overestimating the shear resistance of flexural members raises safety concerns since it might lead to unsafe design practices that ultimately cause the entire structure to collapse. In the same manner, underestimating the shear resistance may give rise to uneconomical designs. In this research, the predictions of various code-based & authorial shear resistance models in terms of their structural performance were assessed through the model uncertainties. According to Gino et al. (2017), identifying and quantifying the uncertainty related to a specific model is of high relevance to structural safety verification in the course of reliability assessment. Uncertainties related to models adequately capture the inconsistency of models’ performance across varied structural conditions. Hence, the extent of conservatism demonstrated by shear models of beams without stirrups is investigated towards structural reliability assessment and calibration. A database of 784 experimental beams without shear reinforcements compiled by Reineck et al. (2013), consisting of beams with varying geometrical properties was investigated in this study. Analyses conducted in this study include a mean values analysis (best-estimate prediction without any form of bias) and a deterministic design value analysis (inclusion of partial safety factor or reduction factor and characteristic material properties). Shear values derived from mean value analysis are used as the input parameter to determine the uncertainty of each model for the same structural condition. Supervised machine learning models based on the architecture of the Artificial Neural Networks, Support Vector Machine, Decision Tree Regressor and Random Forest were also used for shear resistance predictions. Model uncertainty was also derived for machine learning predictive shear models. A comparative analysis between the experimental shear resistance and all considered predictive model was done. Statistical characterization of each model factor in terms of the bias, standard deviation, coefficient of variation and skewness was carried out to evaluate the model’s performance in order to adopt a general probabilistic model for subsequent reliability evaluation. Sensitivity analysis of model uncertainty to parametric variation of input parameters was also carried out with the measured value of correlation. The provision of a carefully calibrated partial factor for model uncertainties that will take into account the uncertainty associated with shear methods is the most efficient management of the reliability performance for any resistance method. To this end, the calibration of a partial factor of safety according to EN 1992-4 was done for models with poor performance in terms of uncertainty performance indexes such as sensitivity to model parameters, a large degree of variability in shear prediction and significant bias in prediction.
Investigation of the structural response of masonry systems using traditional and data-driven numerical techniques.
(2022) Motsa, Siphesihle Mpho.; Drosopoulos, Georgios A.
The understanding of the structural behaviour of masonry structures is of great importance for the preservation of their structural integrity and restoration. Masonry arches are among the oldest structural systems in the world. The failure of these structures can lead to loss of the architectural inheritance and therefore, a full understanding of their structural behaviour is of paramount importance. Over the years, several approaches have been developed for the investigation of failure of masonry structures. Emphasis is given in the heterogeneous nature of masonry (masonry blocks and mortar joints), which imposes a difficulty in simulating the response of this structural type. Continuum damage and discrete models can be adopted to simulate damage in masonry structures. Finite element analysis is one of the numerical tools, which are widely used for this task. In this thesis, a methodology is proposed for the structural evaluation of masonry systems, such as buildings and arches, using nonlinear finite element analysis. Traditional constitutive descriptions, including non-smooth contact mechanics, as well as damage mechanics, are adopted for the investigation of the ultimate, failure response of masonry structures. Within this framework, the existing interfaces between masonry blocks, standing for potential damage surfaces, are simulated using unilateral contact and friction. To capture the compressive damage mode on the blocks, damage plasticity laws are introduced. Compressive and tensile damage plasticity laws can also be used to simulate the failure response of complex masonry systems. A new approach is also provided in the thesis, relying on data-driven structural engineering using machine learning principles. According to this approach, artificial neural networks are adopted to replace time-consuming numerical simulations, providing a fast and computationally efficient evaluation of the failure response for masonry arches. Datasets are built for this purpose, using finite element analysis simulations. For the implementation of the parametric simulations, which are needed for the development of the datasets, programming codes in Python and Matlab are developed, in collaboration with commercial finite element models. The proposed concept can be adopted to predict the mechanical response, failure load and collapse mechanism of masonry arches and thus, it can be used for the structural health monitoring of these structures. To provide a holistic investigation of the structural response, the thesis focuses on the evaluation of both the static structural and the dynamic response of masonry buildings. Case studies in real structural systems are included, highlighting the applicability and efficiency of the proposed methodologies. In particular, the structural response of a three-span masonry arch bridge in Turkey, as well as the response of a seven-span shipyard building in Greece, has been investigated. Among the outcomes of this thesis, is the evaluation of the collapse mechanisms of multi-span masonry arches, as these compare to the collapse mechanisms of single-span arches. It is proved that a four-hinge failure mechanism arises when a vertical load is applied at the middle arch of a three-span masonry arch bridge, which is a typical response observed on single span masonry arches. It is also noted that a hinge-mechanism is the critical failure pattern for discrete models of multi-span masonry arches, under in-plane and out-of-plane loads. For the structural assessment of masonry buildings, it is proved in this thesis that finite element analysis can be used to explain real and possibly undocumented structural damages experienced by the buildings, due to static and/or dynamic actions. An effort is also made in the thesis, to propose an innovative data-driven methodology, aiming to capture the structural response and collapse mechanism of masonry arches. Thus, it is shown how machine learning can be integrated within structural analysis and used to solve the complex problem of the structural evaluation of circular masonry arches. The computational cost of this methodology is significantly reduced, comparing to conventional finite element simulations. The extension of this concept can be adopted for the structural health monitoring of masonry structures.

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Browsing Civil Engineering by Subject "Artificial neural networks."