Kong Fah Tee

Wave Propagation in Auxetic Tetrachiral Honeycombs

This paper describes a numerical and experimental investigation on the flexural wave propagation properties of a novel class of negative Poissons ratio honeycombs with tetrachiral topology. Tetrachiral honeycombs are structures defined by cylinders connected by four tangent ligaments, leading to a negative Poissons ratio (auxetic) behavior in the plane due to combined cylinder rotation and bending of the ribs. A Bloch wave approach is applied to the representative unit cell of the honeycomb to calculate the dispersion characteristics and phase constant surfaces varying the geometric parameters of the unit cell. The modal density of the tetrachiral lattice and of a sandwich panel having the tetrachiral as core is extracted from the integration of the phase constant surfaces, and compared with the experimental ones obtained from measurements using scanning laser vibrometers.

Application of subset simulation in reliability estimation of underground pipelines

This paper presents a computational framework for implementing an advanced Monte Carlo simulation method, called Subset Simulation (SS) for time-dependent reliability prediction of underground flexible pipelines. The SS can provide better resolution for low failure probability level of rare failure events which are commonly encountered in pipeline engineering applications. Random samples of statistical variables are generated efficiently and used for computing probabilistic reliability model. It gains its efficiency by expressing a small probability event as a product of a sequence of intermediate events with larger conditional probabilities. The efficiency of SS has been demonstrated by numerical studies and attention in this work is devoted to scrutinise the robustness of the SS application in pipe reliability assessment and compared with direct Monte Carlo simulation (MCS) method. Reliability of a buried flexible steel pipe with time-dependent failure modes, namely, corrosion induced deflection, buckling, wall thrust and bending stress has been assessed in this study. The analysis indicates that corrosion induced excessive deflection is the most critical failure event whereas buckling is the least susceptible during the whole service life of the pipe. The study also shows that SS is robust method to estimate the reliability of buried pipelines and it is more efficient than MCS, especially in small failure probability prediction.

Maintenance management of offshore structures using Markov process model with random transition probabilities

Abstract The robustness of an offshore engineering design is highly dependent on the maintenance management, where the latter needs a full knowledge of engineering analysis and predictions. An accurate estimation of offshore structural performance with time-varying effect is a keen technical issue. The traditional Markov chain model used for structural strength predictions suffers from the difficulty that some of the measurements or inspection data are largely different from the predicted damage condition. This paper presents a deterioration prediction method for maintenance planning in offshore engineering using the Markov models. Instead of traditional deterministic approaches, the Markov chain model is refined by expressing the transition probabilities as random variables. Through such development, the proposed model is able to estimate an interval for the deterioration of an offshore structure. An existing offshore structure located in South China Sea is used in this study for the demonstration purpose. The selection of transition periods of the Markov chain model is investigated. The use of the stochastic model in the prediction of maintenance timing is also discussed. The results show that the proposed approach can provide more reliable information on structural integrity compared to the conventional method.

Prediction of sulphide build-up in filled sewer pipes

Millions of dollars are being spent worldwide on the repair and maintenance of sewer networks and wastewater treatment plants. The production and emission of hydrogen sulphide has been identified as a major cause of corrosion and odour problems in sewer networks. Accurate prediction of sulphide build-up in a sewer system helps engineers and asset managers to appropriately formulate strategies for optimal sewer management and reliability analysis. This paper presents a novel methodology to model and predict the sulphide build-up for steady state condition in filled sewer pipes. The proposed model is developed using a novel data-driven technique called evolutionary polynomial regression (EPR) and it involves the most effective parameters in the sulphide build-up problem. EPR is a hybrid technique, combining genetic algorithm and least square. It is shown that the proposed model can provide a better prediction for the sulphide build-up as compared with conventional models.

Stochastic failure analysis of the gusset plates in the Mississippi River Bridge

The I-35W Mississippi River Bridge over Minneapolis, Minnesota, collapsed suddenly on August 1, 2007. Previous studies showed that the demand-to-capacity ratio for one of the gusset plates had become extremely high after about 40 years of service of the bridge and therefore, the failure of the gusset plate caused the failure of the whole bridge. A forensic assessment using stochastic reliability analysis is carried out in this research to check whether the collapse of the bridge could have been predicted at the design stage. For this purpose, the probabilities of failure for different types of stresses in the gusset plate are estimated. To consider the uncertainties involved in dead load and live load increments with time, the gamma process concept is employed to model stress increments. It is shown that the probability of failure in the year 2007 was higher than the recommended value. Therefore, it can be concluded that if the results of this study had been available at the design stage, the lack of reliability in 2007 could have been predicted and the collapse of the bridge and its disastrous consequences could have been prevented. It is also concluded that stochastic reliability analysis can be used as a rational tool for failure analysis and reliability assessment of bridges to prevent the risk of collapse.

Reliability analysis of underground pipelines with correlations between failure modes and random variables

Underground pipeline structures may contain multiple failure modes in which any of the modes can lead to a system failure. These failure modes are a time-variant process, and failure rate increases with the lapse of time. The failure modes may be correlated due to common random variables. In many cases, failure modes are assumed to be independent, and underground pipeline failure is evaluated by neglecting correlations between failure modes. However, neglecting correlations may lead to a gross error in pipeline reliability analysis. Correlations between time-dependent failure modes due to corrosion-induced deflection, buckling, wall thrust and bending stress for a buried flexible steel pipe have been assessed in this study. Reliability index and system failure probability have been analysed using Monte Carlo simulation. Parametric analysis indicates that soil modulus, soil density, pipe stiffness and external loading are the most influencing random variables. The estimated reliability can be utilised to develop maintenance strategies during the pipe service lifetime in order to avoid unexpected failure or collapse.

Risk-Cost Optimization of Buried Pipelines Using Subset Simulation

On the basis of time-dependent reliability analysis, a computational framework called subset simulation (SS) has been applied for risk-cost optimization of flexible underground pipeline networks. SS can provide better resolution for rare failure events that are commonly encountered in pipeline engineering applications. Attention in this work is devoted to scrutinize the robustness of SS in risk-cost optimization of pipelines. SS is first employed to estimate the reliability of flexible underground pipes subjected to externally applied loading and material corrosion. Then SS is extended to determine the intervention year for maintenance and to identify the most appropriate renewal solution and renewal priority by minimizing the risk of failure and whole life-cycle cost. The efficiency of SS compared to genetic algorithm has been demonstrated by numerical studies with a view to prevent unexpected failure of flexible pipes at minimal cost by prioritizing maintenance based on failure severity and system reliability. This paper shows that SS is a more robust method in the decision-making process of reliability-based management for underground pipeline networks.

Impact damage detection in composite chiral sandwich panels

This paper demonstrates impact damage detection in a composite sandwich panel. The panel is built from a chiral honeycomb and two composite skins. Chiral structures are a subset of auxetic solids exhibiting counterintuitive deformation mechanism and rotative but not reflective symmetry. Damage detection is performed using nonlinear acoustics,involves combined vibro-acoustic interaction of high-frequency ultrasonic wave and low-frequency vibration excitation. High-and low-frequency excitations are introduced to the panel using a low-profile piezoceramic transducer and an electromagnetic shaker, respectively. Vibro-acoustic modulated responses are measured using laser vibrometry. The methods used for impact damage detection clearly reveal de-bonding in the composite panel. The high-frequency weak ultrasonic wave is also modulated by the low-frequency strong vibration wave when nonlinear acoustics is used for damage detection. As a result frequency sidebands can be observed around the main acoustic harmonic in the spectrum of the ultrasonic signal.

Structural reliability analysis of multiple limit state functions using multi-input multi-output support vector machine

Selecting and using an appropriate structural reliability method is critical for the success of structural reliability analysis and reliability-based design optimization. However, most of existing structural reliability methods are developed and designed for a single limit state function and few methods can be used to simultaneously handle multiple limit state functions in a structural system when the failure probability of each limit state function is of interest, for example, in a reliability-based design optimization loop. This article presents a new method for structural reliability analysis with multiple limit state functions using support vector machine technique. A sole support vector machine surrogate model for all limit state functions is constructed by a multi-input multi-output support vector machine algorithm. Furthermore, this multi-input multi-output support vector machine surrogate model for all limit state functions is only trained from one data set with one calculation process, instead of constructing a series of standard support vector machine models which has one output only. Combining the multi-input multi-output support vector machine surrogate model with direct Monte Carlo simulation, the failure probability of the structural system as well as the failure probability of each limit state function corresponding to a failure mode in the structural system can be estimated. Two examples are used to demonstrate the accuracy and efficiency of the presented method.

Application of receiver operating characteristic curve for pipeline reliability analysis

Structural reliability analysis of buried pipeline systems is one of the fundamental issues for water and wastewater asset managers. Measuring the accuracy of a reliability analysis or a failure prediction technique is an effective approach to enhancing its applicability and provides guidance on selection of reliability or failure prediction methods. The determination of threshold value for a particular pipe failure criterion provides useful information on reliability analysis. However, this threshold value is not always known. In this article, receiver operating characteristic curve has been applied where empirical and nonparametric predictive inference techniques are used to evaluate the accuracy of pipeline reliability analysis and to predict the failure threshold value. Multi-failure conditions, namely, corrosion-induced deflection, buckling, wall thrust and bending stress have been assessed in this article. It is hoped that choosing the optimal operating point on the receiver operating characteristic curve, which involves both maintenance and financial issues, can be ideally implemented by combining the receiver operating characteristic analysis with a formal risk–cost management of underground pipelines.