Developing a robust SHM method for offshore jacket platform using model updating and fuzzy logic system

Structural monitoring is essential for ensuring the structural safety performance during the service life. The process is of paramount importance in case of the offshore jacket-type platforms due to the underwater structural parts subjected to the marine environmental conditions. This work is an experimental investigation on a laboratory model of a jacket platform with the objective of establishing a baseline finite element (FE) model for long-term structural health monitoring for this type of structures. A robust damage diagnosis system is also developed which is less sensitive to both the measurements and the modeling uncertainties. Experimental vibration tests are conducted on a physical platform model to obtain dynamic characteristics and then, the initial FE-model of the intact structure is developed to determine them numerically. Some differences between numerically and experimentally identified characteristics emerge due to various uncertainties in the FE-model and measured vibration data. To minimize these differences, initial FE-model is updated according to the experimental results. The updated FE-model is employed to predict the changes in the dynamic characteristics under variety of damage scenarios which are imposed by reducing the stiffness at the components of the model. Fuzzy logic system (FLS) and probabilistic analysis is developed for linguistic classification of damage and global damage diagnosis. Incorporation of the FLS fault isolation technique into FE-model updating method are proposed and evaluated for two different FLS methods to develop a vigorous damage diagnosis method. The efficiency of the technique is validated by different damage scenarios foreseen on the physical model. This technique is shown to be effective for diagnosing the presence of degradation and quantify it.     

Predicting the resilience of transport infrastructure to a natural disaster using Cox’s proportional hazards regression model

Transport infrastructure is at significant risk of direct damage from extreme climate events such as flooding, where the cost implications of delayed recovery are generally significant. Previous research in this regard has focused on the technical and engineering aspects of infrastructure construction. The risk management of resilient transport infrastructure is poorly considered, and little has been done to quantify the capacity of transport infrastructure to recover from the impact of natural disasters under varying conditions. This paper applies Cox’s proportional hazards regression model to determine the rate of recovery and cumulative probability that recovery occurs for transport infrastructure across regional areas in New South Wales, Australia. Data for post-disaster reconstruction projects over the period 1992–2012 are used to analyze recovery rate against geographic region, natural disaster type and post-disaster transport infrastructure reconstruction cost. Results demonstrate that transport infrastructure recovered slowest when the failure is the result of a flood rather than bushfire or storm, and in regions with a riverine geography. To validate the accuracy of the model, a bootstrap resampling technique is used. The bootstrap result confirms that the model is robust and reasonable.

     

A Hybrid Particle Swarm Optimization and Genetic Algorithm for Model Updating of A Pier-Type Structure Using Experimental Modal Analysis

China Ocean Engineering - Conventional design of pier structures is based on the assumption of fully rigid joints. In practice, the real connections are semi-rigid that cause changes in dynamic...     

Fault diagnosis using noise modeling and a new artificial immune system based algorithm

A new fault classification/diagnosis method based on artificial immune system(AIS) algorithms for the structural systems is proposed. In order to improve the accuracy of the proposed method, i.e., higher success rate, Gaussian and non-Gaussian noise generating models are applied to simulate environmental noise. The identification of noise model, known as training process, is based on the estimation of the noise model parameters by genetic algorithms(GA) utilizing real experimental features. The proposed fault classification/diagnosis algorithm is applied to the noise contaminated features. Then, the results are compared to that obtained without noise modeling. The performance of the proposed method is examined using three laboratory case studies in two healthy and damaged conditions. Finally three different types of noise models are studied and it is shown experimentally that the proposed algorithm with non-Gaussian noise modeling leads to more accurate clustering of memory cells as the major part of the fault classification procedure.     

Earthquake response analysis considering non-proportional damping

Non-proportional damping may be defined as a form of linear viscous damping which introduces coupling between the undamped modal co-ordinate equations of motion. The standard mode superposition method of earthquake response analysis therefore cannot be employed with non-proportionally damped structures. In this paper, several methods for analysing the dynamic response of non-proportional damped structures are outlined. It is concluded that the most efficient procedure is to express the response in terms of a truncated set of undamped modal coordinates and to integrate directly the resulting coupled equations. The effectiveness of the method is demonstrated by a numerical example.     

A robust damage detection method developed for offshore jacket platforms using modified artificial immune system algorithm

Steel jacket-type platforms are the common kind of the offshore structures and health monitoring is an important issue in their safety assessment.In the present study,a new damage detection method is adopted for this kind of structures and inspected experimentally by use of a laboratory model.The method is investigated for developing the robust damage detection technique which is less sensitive to both measurement and analytical model uncertainties.For this purpose,incorporation of the artificial immune system with weighted attributes(AISWA) method into finite element(FE) model updating is proposed and compared with other methods for exploring its effectiveness in damage identification.Based on mimicking immune recognition,noise simulation and attributes weighting,the method offers important advantages and has high success rates.Therefore,it is proposed as a suitable method for the detection of the failures in the large civil engineering structures with complicated structural geometry,such as the considered case study.     

Analysis of the susceptibility of interdependent infrastructures using fuzzy input–output inoperability model: the case of flood hazards in Tehran

Natural Hazards - Advancement in technology has contributed to increment in complexity of systems and infrastructures. Furthermore, it has complicated the management of systems to deal with natural...     

Optimisation of deep mixing technique by artificial neural network based on laboratory and field experiments

ABSTRACT

Ground improvement techniques are inevitable for weak soils that cannot endure the design load imposed by superstructures. Deep mixing technique (DMT) as one of these methods is promising and effective when a deep soil layer with low bearing capacity is encountered. Such deposits are quite common in the South-west of Iran where the studied site is located. In order to validate the influence of DMT on the enhancement of strength, both in-situ and laboratory tests were conducted. Afterwards, a parametric study was carried out to investigate the influence of key factors including cement content, water–cement ratio, curing time and plasticity index (PI) on the performance of DMT. In summary, a total of 192 different conditions were examined in this study by using two methods of 3D plotting and artificial neural networks (ANNs) as the optimisation tool. Results proved the importance of water–cement ratio as a key parameter in DMT. Based on the trained networks, ANN was revealed to give satisfactory predictions on the strength of an improved soil with different admixture conditions. More important, the optimisation made by ANN could determine the specific values for selected key admixture factors to reach a desired strength level with the coefficient of determination higher than 0.85.