Reliability-based code revision for design of pile foundations: Practice in Shanghai, China

AbstractThis paper describes how the code for the design of pile foundations in Shanghai, China is revised based on the reliability theory. With quality static load test data, both within-site and cross-site variabilities for design methods of piles in Shanghai are characterized. It is found that the amount of uncertainties associated with the design of piles in Shanghai is less than the typical values reported in the literature. With the partial factors specified in the previous design code, the reliability indexes of piles designed with empirical methods are in the range of 3.08–4.64, while those of piles designed with the load test-based method are in the range of 5.67–5.89. The load factors in the revised local design code have been reduced according to the national design code. As a result, the resistance factors have been increased in the revised code based on a combination of a reliability analysis and engineering judgment. In the revised design code, the reliability level of piles designed with the empirical methods is similar to that in the previous design code; the reliability level of piles designed with the load test-based method is lowered to achieve cost-effectiveness. Partial factors have been suggested for side and toe resistances based on the reliability theory considering their relative importance as well as the uncertainties involved

Intelligent tracking control of a DC motor driver using self-organizing TSK type fuzzy neural networks

[[abstract]]In this paper, a self-organizing Takagi–Sugeno–Kang (TSK) type fuzzy neural network (STFNN) is proposed. The self-organizing approach demonstrates the property of automatically generating and pruning the fuzzy rules of STFNN without the preliminary knowledge. The learning algorithms not only extract the fuzzy rule of STFNN but also adjust the parameters of STFNN. Then, an adaptive self-organizing TSK-type fuzzy network controller (ASTFNC) system which is composed of a neural controller and a robust compensator is proposed. The neural controller uses an STFNN to approximate an ideal controller, and the robust compensator is designed to eliminate the approximation error in the Lyapunov stability sense without occurring chattering phenomena. Moreover, a proportional-integral (PI) type parameter tuning mechanism is derived to speed up the convergence rates of the tracking error. Finally, the proposed ASTFNC system is applied to a DC motor driver on a field-programmable gate array chip for low-cost and high-performance industrial applications. The experimental results verify the system stabilization and favorable tracking performance, and no chattering phenomena can be achieved by the proposed ASTFNC scheme.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子

Capillary filling with pseudo-potential binary Lattice-Boltzmann model

We present a systematic study of capillary filling for a binary fluid by using a mesoscopic lattice Boltzmann model for immiscible fluids describing a diffusive interface moving at a given contact angle with respect to the walls. The phenomenological way to impose a given contact angle is analysed. Particular attention is given to the case of complete wetting, that is contact angle equal to zero. Numerical results yield quantitative agreement with the theoretical Washburn law, provided that the correct ratio of the dynamic viscosities between the two fluids is used. Finally, the presence of precursor films is experienced and it is shown that these films advance in time with a square-root law but with a different prefactor with respect to the bulk interface.Comment: 13 pages, 8 figures, accepted for publication on The European journal of physics

A probabilistic model for liquefaction triggering analysis using SPT

The liquefaction resistance model developed by Cetin et al. (2004) along with a reference seismic loading model forms a performance function or limit state for liquefaction triggering analysis. Within the framework of the first order reliability method (FORM), the uncertainty of this limit state model is characterized through an extensive series of sensitivity studies using Bayesian mapping functions that were calibrated with a set of quality case histories. With the known model and parameter uncertainties, the probability of liquefaction of a future case can be fairly accurately predicted using the FORM analysis. A procedure is developed to assess the uncertainty associated with the Bayesian mapping function, which was due to use of limited observed data in the development. The effect of this uncertainty on the final assessment of model uncertainty is evaluated, and the variation in the final notional probability obtained from the FORM analysis estimated. Copyright ASCE 2006

Temperature control by chip-implemented adaptive recurrent fuzzy controller designed by evolutionary algorithm

Online adaptive temperature control by field-programmable gate array (FPGA)-implemented adaptive recurrent fuzzy controller (ARFC) chip is proposed in this paper. The RFC is realized according to the structure of Takagi-Sugeno-Kang (TSK)-type recurrent fuzzy network. Direct inverse control configuration is used. To design RFC offline, evolutionary fuzzy controller using the hybrid of the Simplex method and particle swarm optimization (SPSO) is proposed. In SPSO, each RFC corresponds to a particle, and all the free parameters in RFC are optimally searched. We use the PSO to find a good solution globally, and the incorporation of the Simplex method helps find a better solution around the local region of the best solution found by PSO so far. Then, online adaptive temperature control with ARFC chip implemented by FPGA is proposed. In the ARFC chip, the consequent parameters of all rules are all tuned online using gradient descent. To verify the performance of the ARK chip, experiments on a water bath temperature system are performed

Reinforcement Interval Type-2 Fuzzy Controller Design by Online Rule Generation and Q-Value-Aided Ant Colony Optimization

This paper proposes a new reinforcement-learning method using online rule generation and Q-value-aided ant colony optimization (ORGQACO) for fuzzy controller design. The fuzzy controller is based on an interval type-2 fuzzy system (IT2FS). The antecedent part in the designed IT2FS uses interval type-2 fuzzy sets to improve controller robustness to noise. There are initially no fuzzy rules in the IT2FS. The ORGQACO concurrently designs both the structure and parameters of an IT2FS. We propose an online interval type-2 rule generation method for the evolution of system structure and flexible partitioning of the input space. Consequent part parameters in an IT2FS are designed using Q-values and the reinforcement local-global ant colony optimization algorithm. This algorithm selects the consequent part from a set of candidate actions according to ant pheromone trails and Q-values, both of which are updated using reinforcement signals. The ORGQACO design method is applied to the following three control problems: 1) truck-backing control; 2) magnetic-levitation control; and 3) chaotic-system control. The ORGQACO is compared with other reinforcement-learning methods to verify its efficiency and effectiveness. Comparisons with type-1 fuzzy systems verify the noise robustness property of using an IT2FS