Framed tube structures are particularly suitable for tall buildings. Owing to flexural and shear flexibilities of the frame members, the overall bending of the frame is complicated by the occurance of shear lag. The shear lag phenomenon in framed‐tube structures under lateral loads is explained in the literature, but explanation of its origin and comprehensive studies of it is lacking. In this paper an analogy between the shear lag behaviour of a cantilever box representing a uniform framed‐tube building is used for exact analysis of stress and displacement components of perimeter columns. The coefficients of the power series used for the governing equilibrium and compatibility equations are functions of the height and width of the frame panels. Numerical examples are given to demonstrate the accuracy of the proposed method. Copyright © 2002 John Wiley & Sons, Ltd.

High-performance mortars (HPMs) are very compact handmade composites with high compressive strength and low permeability and flexibility. Because of these properties, HPMs are used in special structural and defensive structures. This paper reports on the influence of functionalised multi-walled carbon nanotubes (MWCNTs) on the impact resistance and compressive and flexural strengths of HPM. The functionalised MWCNTs were characterised using Fourier transform infrared (FTIR) spectroscopy and X-ray diffraction (XRD). FTIR analysis confirmed the presence of oxygen-containing groups such as hydroxyl and carboxyl on the surface of oxidised MWCNTs. The XRD results revealed a sharp peak at around 2θ = 26° and a broad peak centred at 2θ = 43°, corresponding to the (002) and (100) Bragg reflection planes respectively. The results of tests on reinforced high-performance mortar containing 0·1 %wt MWCNTs show that the impact resistance, compressive strength and flexural strength were 1400%, 25·58% and 2% higher than those of HPM without MWCNTs. Furthermore, scanning electron microscopy test results show that the presence of MWCNTs has a significant effect on the microstructure of HPM.

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A new neural network (NN) predictive controller (NNPC) algorithm has been developed and tested in the computer simulation of active control of a nonlinear structure. In the present method an NN is used as a predictor. This NN has been trained to predict the future response of the structure to determine the control forces. These control forces are calculated by minimizing the difference between the predicted and desired responses via a numerical minimization algorithm. Since the NNPC is very time consuming and not suitable for real-time control, it is then used to train an NN controller. To consider the effectiveness of the controller on probability of damage, fragility curves are generated. The approach is validated by using simulated response of a 3 story nonlinear benchmark building excited by several historical earthquake records. The simulation results are then compared with a linear quadratic Gaussian (LQG) active controller. The results indicate that the proposed algorithm is completely effective in relative displacement reduction.