Effects of uncertainties on seismic behaviour of optimum designed braced steel frames

Iman Hajirasouliha,Kypros Pilakoutas,Reza K. Mohammadi 국제구조공학회 2016 Steel and Composite Structures, An International J Vol.20 No.2

Concentrically braced steel frames (CBFs) can be optimised during the seismic design process by using lateral loading distributions derived from the concept of uniform damage distribution. However, it is not known how such structures are affected by uncertainties. This study aims to quantify and manage the effects of structural and ground-motion uncertainty on the seismic performance of optimum and conventionally designed CBFs. Extensive nonlinear dynamic analyses are performed on 5, 10 and 15-storey frames to investigate the effects of storey shearstrength and damping ratio uncertainties by using the Monte Carlo simulation method. For typical uncertainties in conventional steel frames, optimum design frames always exhibit considerably less inter-storey drift and cumulative damage compared to frames designed based on IBC-2012. However, it is noted that optimum structures are in general more sensitive to the random variation of storey shear-strength. It is shown that up to 50% variation in damping ratio does not affect the seismic performance of the optimum design frames compared to their code-based counterparts. Finally, the results indicate that the ground-motion uncertainty can be efficiently managed by optimizing CBFs based on the average of a set of synthetic earthquakes representing a design spectrum. Compared to code-based design structures, CBFs designed with the proposed average patterns exhibit up to 54% less maximum inter-storey drift and 73% less cumulative damage under design earthquakes. It is concluded that the optimisation procedure presented is reliable and should improve the seismic performance of CBFs.

Bond strength prediction of steel bars in low strength concrete by using ANN

Sohaib Ahmad,Kypros Pilakoutas,Muhammad M. Rafi,Qaiser U. Zaman 사단법인 한국계산역학회 2018 Computers and Concrete, An International Journal Vol.22 No.2

This paper presents Artificial Neural Network (ANN) models for evaluating bond strength of deformed, plain and cold formed bars in low strength concrete. The ANN models were implemented using the experimental database developed by conducting experiments in three different universities on total of 138 pullout and 108 splitting specimens under monotonic loading. The key parameters examined in the experiments are low strength concrete, bar development length, concrete cover, rebar type (deformed, cold-formed, plain) and diameter. These deficient parameters are typically found in non-engineered reinforced concrete structures of developing countries. To develop ANN bond model for each bar type, four inputs (the low strength concrete, development length, concrete cover and bar diameter) are used for training the neurons in the network. Multi- Layer-Perceptron was trained according to a back-propagation algorithm. The ANN bond model for deformed bar consists of a single hidden layer and the 9 neurons. For Tor bar and plain bars the ANN models consist of 5 and 6 neurons and a single hidden layer, respectively. The developed ANN models are capable of predicting bond strength for both pull and splitting bond failure modes. The developed ANN models have higher coefficient of determination in training, validation and testing with good prediction and generalization capacity. The comparison of experimental bond strength values with the outcomes of ANN models showed good agreement. Moreover, the ANN model predictions by varying different parameters are also presented for all bar types.

Numerical Determination of Moisture Diffusivity in Concrete

Shanker Lal Meghwar,Kypros Pilakoutas,Giacomo Torelli,Maurizio Guadagnini 대한토목학회 2022 KSCE Journal of Civil Engineering Vol.26 No.9

Accurate modelling of moisture diffusion is essential, as it dominates the drying process in concrete and governs the development of shrinkage strains that affect the short- and long-term deformation and cracking behaviour of structural elements. Key models available in the literature use porosity as the main parameter to predict the diffusivity of the material. Although physically sound, this approach is difficult to apply in practice, as the in-situ determination of concrete porosity is challenging. To address this, the present study uses readily available quantities (namely w/c ratio and concrete maturity) as primary material modelling parameters and investigates the effects of pore relative humidity and ambient temperature on the diffusivity properties of concrete using inverse numerical analysis and available experimental data. As a result, a diffusion modelling approach that can be readily used in practical applications is proposed and verified through finite element analyses. The results show that numerical predictions are in good agreement with experimental data. Specifically, the model is capable of capturing the effects of w/c ratio, concrete maturity and thermal conditions on the evolution of the moisture profile within drying concrete elements. The model can be used to determine drying shrinkage strains with a high degree of accuracy, thereby allowing for a more realistic assessment of crack evolution in drying concrete elements and its effects on overall structural performance.

Long-term monitoring of a hybrid SFRC slab on grade using recycled tyre steel fibres

Baricevic, Ana,Grubor, Martina,Paar, Rinaldo,Papastergiou, Panos,Pilakoutas, Kypros,Guadagnini, Maurizio Techno-Press 2020 Advances in concrete construction Vol.10 No.6

This paper presents one of the demonstration projects undertaken during the FP7 EU-funded Anagennisi project (Innovative reuse of all tyre components in concrete-2014-2017) on a full-scale (30 m×40 m, thickness: 0.2 m) Steel Fibre Reinforced Concrete (SFRC) slab-on-grade using a blend of manufactured steel fibres (MSF) and Recycled Tyre Steel Fibres (RTSF). The aim of the project was to assess the use of RTSF in everyday construction practice. The Anagennisi partners, Dulex Ltd in collaboration with Gradmont-Gradacac Ltd and University of Zagreb, designed, cast and monitored the long-term shrinkage deformations of the indoor slab-on-grade slab at Gradmont's precast concrete factory in Gradacac, Bosnia and Herzegovina. A hybrid RTSF mix (20 kg/㎥ of MSF+10 kg/㎥ of RTSF) was used to comply with the design criteria which included a maximum load capacity of 20 kN/㎡. The slab was monitored for one year using surveying equipment and visual inspection of cracks. During the monitoring period, the slab exhibited reasonable deformations (a maximum displacement of 3.3 mm for both, horizontal and vertical displacements) whilst after five years in use, the owners did not report any issues and were satisfied with the construction methodology and materials used. This work confirms that RSTF is a viable and sustainable solution for slab-on-grade applications.

Predicting the shear strength of reinforced concrete beams using Artificial Neural Networks

Panagiotis G. Asteris,Danial J. Armaghani,George D. Hatzigeorgiou,Chris G. Karayannis,Kypros Pilakoutas 사단법인 한국계산역학회 2019 Computers and Concrete, An International Journal Vol.24 No.5

In this research study, the artificial neural networks approach is used to estimate the ultimate shear capacity of reinforced concrete beams with transverse reinforcement. More specifically, surrogate approaches, such as artificial neural network models, have been examined for predicting the shear capacity of concrete beams, based on experimental test results available in the pertinent literature. The comparison of the predicted values with the corresponding experimental ones, as well as with available formulas from previous research studies or code provisions highlight the ability of artificial neural networks to evaluate the shear capacity of reinforced concrete beams in a trustworthy and effective manner. Furthermore, for the first time, the (quantitative) values of weights for the proposed neural network model, are provided, so that the proposed model can be readily implemented in a spreadsheet and accessible to everyone interested in the procedure of simulation.