Browsing by Author "Motsa, Siphesihle Mpho."

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Investigation of the structural behaviour of Megalithic Mnajdra Monument.
(2018) Motsa, Siphesihle Mpho.; Drosopoulos, Georgios A.
The Maltese megalithic temples are regarded as one of the oldest structures from early civilization dating back to about the 4th millennium. These megalithic structures provide valuable information due to their complexity of geometry and the way they were constructed, and they also serve as one of the main tourist attraction areas in the Maltese islands. The areas in which these megalithic structures are found have been listed as UNESCO World Heritage Sites. In this study, the structural behaviour of the middle temple of the Mnajdra Megalithic temple is investigated. First, the geometry of the temple was obtained from a cloud of points using radar-laser scanning. The mechanical material properties of the megaliths and soil were obtained from published experimental research on similar monuments. A finite element model was created whereby nonlinear contact mechanics was applied since unilateral contact between parts is the major stress-transfer mechanism. Simulations from eigenmode analysis show various natural frequencies and shape modes of the monument which can be validated from experimental data obtained from an ambient vibration monitor. Results obtained from structural analysis show the major stress transfer points between the megaliths, the response of the structure under selected loading scenarios as well as potential collapse mechanisms.
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.