Jan-Erik Jonasson

A state-of-the-art review of structural control systems:

The utilization of structural control systems for alleviating the responses of civil engineering structures, under the effects of different kinds of dynamics loadings, has become a standard technology, although there are still numerous research approaches for advancing the effectiveness of these methodologies. The aim of this article is to review the state-of-the-art technologies in structural control systems by introducing a general literature review for all types of vibrations control systems that have appeared up to now. These systems can be classified into four main groups: (a) passive; (b) semi-active; (c) active; and (d) hybrid systems, based on their operational mechanisms. A brief description of each of these main groups and their subgroups, with their corresponding advantages and disadvantages, is also given. This article will conclude by providing an overview of some innovative practical implementations of devices that are able to demonstrate the potential and future direction of structural control systems in civil engineering.

Investigation of changes in modal characteristics before and after damage of a railway bridge: a case study

The linear, time-invariant transfer function has been utilised for the construction of frequency response function , based on the ambient vibration measurements. The results presented here indicated the possibility to identify and localise damages in steel railway bridges from the variations in the modal characteristics of the structure. The comparison between the modal characteristics for the healthy and collapsed bridge confirmed that damage had been existed. The abnormal percentage of change in modal damping, between the healthy and any other condition for a structure, can be regarded as a serious indicator for early stages of damage, while the high percentage of change in modal damping can clearly indicate the existence of damage in that structure. The average ratio of change in the damping ratio from the healthy to the collapsed bridge was about 206% and this ratio could be regarded as an index for the existence of a serious damage in steel bridges, which needs further evaluation in other test cases.

Prediction of restraint in second cast sections of concrete culverts using artificial neural networks

Estimation of restraint is very important for accurately predicting the risk of early thermal and shrinkage cracking in concrete structures. The stress in young concrete is affected by changes in its dimensions during hydration and the restraint imposed by adjoining structures. In concrete culverts, the restraints from existing structures acting upon the first and second casting sections to be cast are different, causing them to exhibit different early cracking behaviour. This work presents a new method for predicting restraint in complex concrete structures using artificial neural networks (ANNs). Finite element calculations were performed to predict restraint in 108 slabs, 324 walls and 972 roofs from second sections of concrete culverts, and the results obtained were used to train and validate ANN models. The ANN models were then used to study the effects of varying selected parameters (the thickness and width of the roof and slab, the thickness and height of the walls, and the length of the culvert section) on the predicted restraint. Mathematical expressions for predicting restraint values in slabs, walls and roofs were derived based on the ANN models’ output and implemented in an Excel spreadsheet that provides a simple way of predicting restraint in practical applications. Restraint values predicted in this way agree well with the results of finite-element calculations.

Reduction of Early-Age Crack Risks in Concrete Walls by Using a New Casting Technique

Abstract Volumetric changes in early-age concrete that are restrained might lead to cracks. The degree of restraint is influenced by the casting sequence and the dimensions of the castings. In the current study, a new casting technique is proposed to reduce the restraint in newly cast concrete by following a new method of arranging the structural joint with the existing old concrete. The proposed technique is valid for the typical wall-on-slab structure using one structural joint. According to this casting method the lower part of the wall is cast together with the slab, which is called a kicker. Thereafter, the behaviour of the structure changes from a typical wall-on-slab case to a typical wall-on-wall case. It has been proven based on the beam theory and demonstrated by numerical calculations that there is a clear reduction in the restraint from the slab to the wall by using the kickers. In this paper, different kicker heights are studied with the aim of determining the minimum restraint in the upper part of the wall cast in contact with the kicker. The technique using kickers is compared with common measures used in the field to avoid cracking, such as using cooling pipes in the new casting and/or heating cables in the adjoining old concrete. The presented method is both cost and time effective, as it opens up the possibility to use larger structural length for each casting sequence.

Using Artificial Neural Networks to Predict the Restraint in Concrete Culverts at Early Age

Abstract Estimation of restraint is very important for accurate prediction of the risk of concrete cracking at early age. This study predicts the restraint in 324 walls and 972 roofs of concrete culverts. A parametric study included the thickness and width of the roofs, thickness and height of the walls, thickness and width of the slab and length of the structures. Each parameter increased or decreased the restraint in the walls and the roofs. The calculation of the restraint was done elastically by the finite element (FE) method. The results were used by an artificial neural network (ANN) tool, where, first, an influential percentage was investigated as input parameter on the restraint prediction. Equations were derived by the ANN model to calculate the restraint in the walls and the roofs. It was then used in a spreadsheet to calculate the restraint and compare the result with the result from the FE calculations, which showed a good agreement between the ANN model and the FE calculations.

A COMPARATIVE STUDY ON THE IDENTIFICATION OF BUILDING NATURAL FREQUENCIES BASED ON PARAMETRIC MODELS

The analysis and design of civil engineering structures is a complex problem, which is based on many assumptions to simplify these operations. This in turn, leads to a difference in the structural behavior between calculations based models and real structures. Structural identification was proposed by many researchers as a tool to reduce this difference between models and actual structures. Moreover, Parametric models and non-parametric models were used intensively for system identification by many researchers. In this research effort, the system identification concept is utilized to identify the natural frequencies for a steel building’s frames. Different black box linear parametric models such as Transfer Function model (TF), Auto-Regressive model with eXternal input model (ARX), Auto-Regressive Moving Average with eXternal input (ARMAX) model, Output Error model structure (OE), and Box-Jenkins model (BJ) were examined for identifying the first 10th natural frequencies for the building’s frames, based on simulation results. Abaqus 6.12 finite-element software was utilized to perform the time history analysis for the examples and the obtained responses at one point of the roofs (assumed as a sensor) were further processed by the parametric models to obtain the building’s natural frequencies based on the Abaqus time history analysis results (assumed as a measurements). After that, Abaqus 6.12 was utlized again to perform another analysis, which is called frequency analysis to obtain the building’s natural frequencies and mode shapes based on the stiffness and mass (not the measurements) of the buildings. The results showed that the linear parametric models TF, ARX, ARMAX, OE, and BJ are robust to identify the natural frequencies of building and they are recommend for future work.