In this paper, effect of torsion on the inelastic responses of structures resting on a nonlinear flexible medium is studied. Similar studies have shown that soil-structure interaction can result in augmentation of nonlinear responses of... more
In this paper, effect of torsion on the inelastic responses of structures resting on a nonlinear flexible medium is studied. Similar studies have shown that soil-structure interaction can result in augmentation of nonlinear responses of lower stories. The focus here is on the plan-wise distribution of inelastic responses of a torsional structure considering soil-structure interaction (SSI). For this purpose, 4, 8, and 12-story steel structures consisting of special moment frames are considered on a relatively soft soil. A nonlinear set of non-uniform springs is used for modeling of SSI. For each building, different structural responses are calculated under 11 suitably scaled earthquake ground motions at the design basis and maximum considered earthquake hazard levels. The mass eccentricity ratio is varied from zero to 30%. The inelastic responses include the story drift ratios and distribution of plastic hinge rotations and performance levels of each story and frame. The results clearly show that SSI increases the drift ratio of the first story up to 30% regardless of the eccentricity value. On the other hand, SSI amplified the cumulative plastic hinge rotation of the upper stories, especially in the taller buildings. The maximum value of amplification exceeded 3 and it was very sensitive to the extent of eccentricity. The local amplification effect of combination of torsion and SSI was much more severe where it reached values over 8 in the outer frames of the buildings, with SSI having the larger share in amplification.
This thesis presents the application of Model Predictive Controller (MPC) on Automatic Transmission (AT) system clutch to clutch transients. This research describes the advance predictive controller applied to optimize the clutch to... more
This thesis presents the application of Model Predictive Controller (MPC) on Automatic Transmission (AT) system clutch to clutch transients. This research describes the advance predictive controller applied to optimize the clutch to clutch shift transient during torque phase and speed phase in order to control the shifting dynamics for a better shift quality and engagement performance. In this research, a high order dynamic model of transmission system which includes the hydraulic dynamics of actuator has been analyzed in order to design multiple Model Predictive Controller (MPC). Simulation results obtained considering upshift maneuvers with engine torque management applied and shows the effectiveness of the proposed control strategy. The analytical solution presented permits to tune the torque phase and speed phase of shifting transient in an optimized way that is easier than previous strategy based on look-up tables and PID controller.
This paper describes the complete dynamic model of a flexible link-flexible joint (FLFJ) manipulator clamped at its actuated base and carrying a payload at its end point. Dynamic model is derived based on combined Euler-Lagrangian and... more
This paper describes the complete dynamic model of a flexible link-flexible joint (FLFJ) manipulator clamped at its actuated base and carrying a payload at its end point. Dynamic model is derived based on combined Euler-Lagrangian and assumed modes approaches. In particular, damping and friction effects have been included in addition to rotor inertia and joint flexibility while modeling. This model is complete even if the effect of gravity and shear deformation are neglected because of the flexible nature of the link. Numerical Simulations are performed in time domain with bang-bang torque input applied at its actuator to show the free vibration behavior of the modeled system. Performance of the system is evaluated by varying payloads and the effect of joint flexibility and damping is also addressed.
In this paper we examine the forecast accuracy of four univariate time series models for 47 macroeconomic variables of the G7 economies. The models considered are the linear autoregressive model, the smooth transition autoregressive... more
In this paper we examine the forecast accuracy of four univariate time series models for 47 macroeconomic variables of the G7 economies. The models considered are the linear autoregressive model, the smooth transition autoregressive model, and two neural network models. The two neural network models are different because they are specified using two different techniques. Forecast accuracy is assessed in a number of ways, comprising evaluation of point, interval and density forecasts. The results indicate that the linear autoregressive and the smooth transition autoregressive model have the best overall performance. Positive results for the nonlinear smooth transition autoregressive model may be largely due to the fact that linearity is tested before building any nonlinear model. This implies that a nonlinear model is employed only when there is a need for it, which makes the risk of fitting unidentified models to the data relatively low.
This paper discusses the simulation of the structural performance of a four-story school building in Sankhu, Nepal after the 2015 Gorkha Earthquake. The structure had a masonry-infilled reinforced concrete frame, which was severely... more
This paper discusses the simulation of the structural performance of a four-story school building in Sankhu, Nepal after the 2015 Gorkha Earthquake. The structure had a masonry-infilled reinforced concrete frame, which was severely damaged during the earthquake. The concentration of damage in the south end of the ground story indicates that the frame exhibited torsional response to the ground excitation. The seismic performance of the building is simulated in this study with a three-dimensional model of the building which utilizes the strut modeling approach for infilled frames. The struts are calibrated using a novel approach which is based on a recently proposed simplified analytical tool for such structures. The simplified tool is validated with detailed FE models that combine the smeared and the discrete crack modeling approaches. The paper discusses the accuracy of the numerical model in simulating the seismic performance and in estimating the identified modal properties of the damaged building. The comparison indicates that the model, when subjected to the ground motion recorded in close proximity during the Gorkha earthquake, develops a similar damage pattern as the actual structure, while its modal properties match well with those estimated from the ambient vibration recordings obtained during a reconnaissance trip.
This paper introduces a new method for improving nonlinear modeling performance in online learning by using functional link-based models. The proposed algorithm is capable of selecting the useful nonlinear elements resulting from the... more
This paper introduces a new method for improving nonlinear modeling performance in online learning by using functional link-based models. The proposed algorithm is capable of selecting the useful nonlinear elements resulting from the functional expansion, while setting to zero the ones that does not bring any improvement of the modeling performance. This allows to reduce any gradient noise due to a possible overestimate of the solution, thus preventing any overfitting phenomena. The proposed model is assessed in several nonlinear identification problems, including different levels of nonlinearity, showing significant improvements.
