Assessment and identification of concrete box-girder bridges properties using surrogate model calibration: case study: El Tablazo bridge

Dissertação de mestrado integrado em Engenharia CivilThis work consists in identifying and assessing the properties in a pre-stressed concrete bridge related to material, geometry and physic sources, through a surrogate model. The participation of this mathematical model allows to generate a relationship between bridge properties and its dynamic response, with the purpose of creating a tool to predict the analytical values of the studied properties from measured eigenfrequencies; in this case, it is introduced the identification of damage scenarios, giving the application for validate the generated metamodel (Artificial Neural Network - ANN). A FE model is developed to simulate the studied structure, a Colombian bridge called El Tablazo, one of the higher in the country of this type (box-girder bridge), with a total length of 560 meters, located on the Sogamoso riverbed in the region of Santander - Colombia. Once the damage scenarios are defined, this work allows to indicate the basis for futures plans of structural health monitoring.Este trabalho consiste em identificar e avaliar as propriedades de uma ponte em betão pré-esforçado em relação ao material, geometria e características físicas através de um metamodelo. A participação deste modelo matemático permite gerar uma relação entre as propriedades da ponte e sua resposta dinâmica, com o objetivo de criar uma ferramenta para prever os valores analíticos das propriedades estudadas a partir de frequências próprias medidas; neste caso, é introduzida a identificação de cenários de dano, dando uma aplicação para validar o metamodelo (Rede Neural Artificial - ANN). Um modelo de elemento finito é desenvolvido para simular a estrutura estudada, uma ponte colombiana chamada El Tablazo, uma das que apresenta maior altura do país em seu tipo (pontes em viga-caixão), com um comprimento total de 560 metros, localizada no rio Sogamoso, na região de Santander - Colômbia. Uma vez que os cenários de dano são definidos, a tese permite indicar a base para os planos futuros de monitoramento da saúde estrutural

Numerical computation for vibration characteristics of long-span bridges with considering vehicle-wind coupling excitations based on finite element and neural network models

CA (Cellular Automaton) model was applied to the simulation of random traffic flow to develop a model considering the randomness of traffic flow and apply it to wind-vehicle-bridge coupling vibration. Finite element and neural network models were adopted respectively to numerically compute the vibration characteristics of bridges under wind and vehicle loads, verify the correctness of model. Subspace iteration method was used for the modal analysis of bridges. Natural frequencies of the top 8 orders were 0.21 Hz, 0.27 Hz, 0.36 Hz, 0.45 Hz, 0.56 Hz, 0.66 Hz, 0.87 Hz and 1.02 Hz respectively. The vibration frequency of the long-span bridge was consistent with the vibration characteristics of large-scale complex structures. Natural modes mainly reflected the torsion and bending of main beam and the swinging vibration of side and main towers. Fluctuation wind time-history presented periodic characteristics. The maximum and minimum values of fluctuation wind were about 20 m/s and –20 m/s respectively. The target and simulation values of power spectral density of wind speed were basically the same in change trend, which indicated that the fluctuation wind time-history computed in this paper was reliable. The model of dense traffic flow based on CA more truly described the running status like accelerating, decelerating and changing lanes of vehicles on the bridge, also contained the density information of vehicles and more truly reflected traffic characteristics. Vibration accelerations of the long-span bridge were symmetrically distributed. Vibration acceleration of central position in the left main span was the largest and near 50 cm/s2; vibration acceleration on the main tower was the smallest. The curve of vibration displacement with considering wind loads presented some fluctuations, while the vibration displacement of bridges without considering wind loads was very smooth. In addition, the amplitude of vibration displacement without considering wind loads moved laterally towards the left compared with that with considering wind loads. Therefore, wind loads must be considered when the vibration characteristics of the long-span bridge were computed. Otherwise, the accuracy of computational results would be reduced. It only took 0.5 hours to use neural network to predict the vibration acceleration of the long-span bridge. In the case of the same computer performance, it took 5 hours to use finite element model to predict the vibration acceleration of the long-span bridge. The advantage of neural network model in predicting the performance of large-scale complex structures like a long-span bridge could be obviously found. In the future, we will consider using neural network model to systematically study and optimize the long-span bridge

Direct and inverse problems on free vibration analysis of cracked non-uniform beams carrying spring-mass systems by finite element method

This paper presents an analytical approach to investigate the free vibration analysis of cracked non-uniform beam carrying spring-mass systems by finite element method and illustrates a valid and reliable damage identification method which using hybrid neural genetic technique. Firstly, based on the finite element method, the dynamic characteristics of non-uniform cracked beam carrying spring-mass systems are obtained. Then, the first five frequencies are used as input parameters by combining genetic algorithm with neural network to identify the damage. Finally, Numerical simulations of direct and inverse problems of non-uniform cracked beams carrying a spring-mass system are carried out

Damage identification in bridge structures : review of available methods and case studies

Bridges are integral parts of the infrastructure and play a major role in civil engineering. Bridge health monitoring is necessary to extend the life of a bridge and retain safety. Periodic monitoring contributes significantly in keeping these structures operational and extends structural integrity. Different researchers have proposed different methods for identifying bridge damages based on different theories and laboratory tests. Several review papers have been published in the literature on the identification of damage and crack in bridge structures in the last few decades. In this paper, a review of literature on damage identification in bridge structures based on different methods and theories is carried out. The aim of this paper is to critically evaluate different methods that have been proposed to detect damages in different bridges. Different papers have been carefully reviewed, and the gaps, limitations, and superiority of the methods used are identified. Furthermore, in most of the reviews, future applications and several sustainable methods which are necessary for bridge monitoring are covered. This study significantly contributes to the literature by critically examining different methods, giving guidelines on the methods that identify the damages in bridge structures more accurately, and serving as a good reference for other researchers and future works

Data-driven model-based approaches to condition monitoring and improving power output of wind turbines

The development of the wind farm has grown dramatically in worldwide over the past 20 years. In order to satisfy the reliability requirement of the power grid, the wind farm should generate sufficient active power to make the frequency stable. Consequently, many methods have been proposed to achieve optimizing wind farm active power dispatch strategy. In previous research, it assumed that each wind turbine has the same health condition in the wind farm, hence the power dispatch for healthy and sub-healthy wind turbines are treated equally. It will accelerate the sub-healthy wind turbines damage, which may leads to decrease generating efficiency and increases operating cost of the wind farm. Thus, a novel wind farm active power dispatch strategy considering the health condition of wind turbines and wind turbine health condition estimation method are the proposed. A modelbased CM approach for wind turbines based on the extreme learning machine (ELM) algorithm and analytic hierarchy process (AHP) are used to estimate health condition of the wind turbine. Essentially, the aim of the proposed method is to make the healthy wind turbines generate power as much as possible and reduce fatigue loads on the sub-healthy wind turbines. Compared with previous methods, the proposed methods is able to dramatically reduce the fatigue loads on subhealthy wind turbines under the condition of satisfying network operator active power demand and maximize the operation efficiency of those healthy turbines. Subsequently, shunt active power filters (SAPFs) are used to improve power quality of the grid by mitigating harmonics injected from nonlinear loads, which is further to increase the reliability of the wind turbine system

Wind turbine power output short-term forecast : a comparative study of data clustering techniques in a PSO-ANFIS model

Abstract:The emergence of new sites for wind energy exploration in South Africa requires an accurate prediction of the potential power output of a typical utility-scale wind turbine in such areas. However, careful selection of data clustering technique is very essential as it has a significant impact on the accuracy of the prediction. Adaptive neurofuzzy inference system (ANFIS), both in its standalone and hybrid form has been applied in offline and online forecast in wind energy studies, however, the effect of clustering techniques has not been reported despite its significance. Therefore, this study investigates the effect of the choice of clustering algorithm on the performance of a standalone ANFIS and ANFIS optimized with particle swarm optimization (PSO) technique using a synthetic wind turbine power output data of a potential site in the Eastern Cape, South Africa. In this study a wind resource map for the Eastern Cape province was developed. Also, autoregressive ANFIS models and their hybrids with PSO were developed. Each model was evaluated based on three clustering techniques (grid partitioning (GP), subtractive clustering (SC), and fuzzy-c-means (FCM)). The gross wind power of the model wind turbine was estimated from the wind speed data collected from the potential site at 10 min data resolution using Windographer software. The standalone and hybrid models were trained and tested with 70% and 30% of the dataset respectively. The performance of each clustering technique was compared for both standalone and PSO-ANFIS models using known statistical metrics. From our findings, ANFIS standalone model clustered with SC performed best among the standalone models with a root mean square error (RMSE) of 0.132, mean absolute percentage error (MAPE) of 30.94, a mean absolute deviation (MAD) of 0.077, relative mean bias error (rMBE) of 0.190 and variance accounted for (VAF) of 94.307. Also, PSO-ANFIS model clustered with SC technique performed the best among the three hybrid models with RMSE of 0.127, MAPE of 28.11, MAD of 0.078, rMBE of 0.190 and VAF of 94.311. The ANFIS-SC model recorded the lowest computational time of 30.23secs among the standalone models. However, the PSO-ANFIS-SC model recorded a computational time of 47.21secs. Based on our findings, a hybrid ANFIS model gives better forecast accuracy compared to the standalone model, though with a trade-off in the computational time. Since, the choice of clustering technique was observed to play a vital role in the forecast accuracy of standalone and hybrid models, this study recommends SC technique for ANFIS modeling at both standalone and hybrid models

A State Evaluation Method for Solder Layer in Mosfet

MOSFET is the core component in power equipment. It is widely used in electrical vehicles (EV), wind generation, rail transit and so on. The long-term impact of temperature and stress cause fatigue in the device during operation. Because of the low melting point of 96.5Sn3.5Ag, solder layer aging and failure is one of the main failure modes. So, it is important to figure out the failure mechanism and the effects of defects in the solder layer. A finite element (FE) model considered the temperature dependence of materials was built in COMSOL software to support the subsequent studies. Effects of voids in solder layer and fatigue are studied and analyzed based on the FE model. The results show the junction temperature, case temperature, on-resistance and thermal resistance between junction and case increase with the rise of voids’ areas and fatigue degree. Besides that, all of them have a similar trend, which means on-resistance can be a criterion for thorough failure replacing the thermal resistance. And the on-resistance is more sensitive than thermal resistance because its growth rate is much higher than that of thermal resistance. Based on the simulation and analyzed, on-resistance, case temperature and on-current were selected as the characteristic parameter to reflect the healthy state of MOSFET. They were used as the inputs for the evaluation model. And the growth rate of on-resistance was chosen as the output parameter. Combine the failure rate curve, the range from health to thorough failure was be divided into five pieces with different intervals. For evaluation, adaptive neuro-fuzzy inference system (ANFIS) was adopted to establish the model. By validation and comparing with some common classification algorithms, it was verified and showed high accuracy

Deterministic and Probabilistic Risk Management Approaches in Construction Projects: A Systematic Literature Review and Comparative Analysis

Risks and uncertainties are inevitable in construction projects and can drastically change the expected outcome, negatively impacting the project’s success. However, risk management (RM) is still conducted in a manual, largely ineffective, and experience-based fashion, hindering automation and knowledge transfer in projects. The construction industry is benefitting from the recent Industry 4.0 revolution and the advancements in data science branches, such as artificial intelligence (AI), for the digitalization and optimization of processes. Data-driven methods, e.g., AI and machine learning algorithms, Bayesian inference, and fuzzy logic, are being widely explored as possible solutions to RM domain shortcomings. These methods use deterministic or probabilistic risk reasoning approaches, the first of which proposes a fixed predicted value, and the latter embraces the notion of uncertainty, causal dependencies, and inferences between variables affecting projects’ risk in the predicted value. This research used a systematic literature review method with the objective of investigating and comparatively analyzing the main deterministic and probabilistic methods applied to construction RM in respect of scope, primary applications, advantages, disadvantages, limitations, and proven accuracy. The findings established recommendations for optimum AI-based frameworks for different management levels—enterprise, project, and operational—for large or small data sets

A State Evaluation Method for Solder Layer in MOSFET

MOSFET is the core component in power equipment. It is widely used in electrical vehicles (EV), wind generation, rail transit and so on. The long-term impact of temperature and stress cause fatigue in the device during operation. Because of the low melting point of 96.5Sn3.5Ag, solder layer aging and failure is one of the main failure modes. So, it is important to figure out the failure mechanism and the effects of defects in the solder layer. A finite element (FE) model considered the temperature dependence of materials was built in COMSOL software to support the subsequent studies. Effects of voids in solder layer and fatigue are studied and analyzed based on the FE model. The results show the junction temperature, case temperature, on-resistance and thermal resistance between junction and case increase with the rise of voids’ areas and fatigue degree. Besides that, all of them have a similar trend, which means on-resistance can be a criterion for thorough failure replacing the thermal resistance. And the on-resistance is more sensitive than thermal resistance because its growth rate is much higher than that of thermal resistance. Based on the simulation and analyzed, on-resistance, case temperature and on-current were selected as the characteristic parameter to reflect the healthy state of MOSFET. They were used as the inputs for the evaluation model. And the growth rate of on-resistance was chosen as the output parameter. Combine the failure rate curve, the range from health to thorough failure was be divided into five pieces with different intervals. For evaluation, adaptive neuro-fuzzy inference system (ANFIS) was adopted to establish the model. By validation and comparing with some common classification algorithms, it was verified and showed high accuracy