Damage Level Prediction of Pier using Neuro-Genetic Hybrid

Generally, long span bridges have multiple columns as known as piers to support the stability of the bridge. The pier is the most vulnerable part of the deck against the earthquake load. The study aims to predict the performance of the pier on the bridge structure subject to earthquake loads using a Neuro-Genetic Hybrid. The mix design of the Back Propagation Neural Networks (BPNN) and Genetic Algorithm (GA) method obtained the optimum-weight factors to predict the damage level of a pier. The input of Neuro-Genetic hybrid consists of 17750 acceleration-data of bridge responses. The outputs are the bridge-damage levels based on FEMA 356. The categorize of a damage level was divided into four performance levels of the structure such as safe, immediate occupancy, life safety, and collapse prevention. Bridge responses and performances have resulted through analysis of Nonlinear Time History. The best of Mean Squared Error and Regression value for the Neuro-Genetic hybrids method are 0.0041 and 0.9496 respectively at 50000 epochs for the testing process.  The Regression value denotes the predicted damage values more than 90% closer to the actual damage values. Thus, the damage level prediction of the pier in this study offers as an alternative to structural control and monitor of bridges

Application of Genetic Algorithm in design of arch bridge

Topic of this article is application of Genetic Algorithm (GA) as a method of global optimization for determining characteristic dimensions of arch concrete bridge. Implementation of genetic algorithm for designing elements of an arch bridge with assign static scheme, span and quality of material is conducted in software Matlab. Goal of this application is to determine dimensions of cross-section and rise of concrete arch with minimum use of material and with carry out stress control in characteristic cross sections

Structural Health Evaluation of Arch Bridge by Field Test and Optimized BPNN Algorithm

Arch bridges play an important role in rural roads in China. Due to insufficient funds and a lack of management techniques, many rural arch bridges are in a state of disrepair, unable to meet the increasing transportation needs. Thus, it is of great significance to develop a set of rapid and economic damage identification procedures for the management and maintenance of old arch bridges. Sanliushui Bridge, located in Chenggu County, Hanzhong, is selected as a model case. Field tests and numerical simulations were carried out to identify the damage states of Sanliushui Bridge. The sum square of wavelet packet energy change rate, a damage identification index based on wavelet packet analysis method was implemented to process the measured data of the load test and the simulated data of the numerical calculation model with assumed damage. BPNN, GA-BPNN, PSO-BPNN and test data analysis are adopted to compare the measured data with the simulated data to quantitatively identify the damage degree of the selected bridge. By comparing the results of the two methods mentioned above, it is found that the proposed damage identification approach realized a precise damage identification of the selected arch bridges

Structural Health Evaluation of Arch Bridge by Field Test and Optimized BPNN Algorithm

Arch bridges play an important role in rural roads in China. Due to insufficient funds and a lack of management techniques, many rural arch bridges are in a state of disrepair, unable to meet the increasing transportation needs. Thus, it is of great significance to develop a set of rapid and economic damage identification procedures for the management and maintenance of old arch bridges. Sanliushui Bridge, located in Chenggu County, Hanzhong, is selected as a model case. Field tests and numerical simulations were carried out to identify the damage states of Sanliushui Bridge. The sum square of wavelet packet energy change rate, a damage identification index based on wavelet packet analysis method was implemented to process the measured data of the load test and the simulated data of the numerical calculation model with assumed damage. BPNN, GA-BPNN, PSO-BPNN and test data analysis are adopted to compare the measured data with the simulated data to quantitatively identify the damage degree of the selected bridge. By comparing the results of the two methods mentioned above, it is found that the proposed damage identification approach realized a precise damage identification of the selected arch bridges

An overview on structural health monitoring: From the current state-of-the-art to new bio-inspired sensing paradigms

In the last decades, the field of structural health monitoring (SHM) has grown exponentially. Yet, several technical constraints persist, which are preventing full realization of its potential. To upgrade current state-of-the-art technologies, researchers have started to look at nature’s creations giving rise to a new field called ‘biomimetics’, which operates across the border between living and non-living systems. The highly optimised and time-tested performance of biological assemblies keeps on inspiring the development of bio-inspired artificial counterparts that can potentially outperform conventional systems. After a critical appraisal on the current status of SHM, this paper presents a review of selected works related to neural, cochlea and immune-inspired algorithms implemented in the field of SHM, including a brief survey of the advancements of bio-inspired sensor technology for the purpose of SHM. In parallel to this engineering progress, a more in-depth understanding of the most suitable biological patterns to be transferred into multimodal SHM systems is fundamental to foster new scientific breakthroughs. Hence, grounded in the dissection of three selected human biological systems, a framework for new bio-inspired sensing paradigms aimed at guiding the identification of tailored attributes to transplant from nature to SHM is outlined.info:eu-repo/semantics/acceptedVersio

Reliability Based Design Optimization Of Load And Rating Models For Bridge Structures In Michigan

The main objective of this study is to develop optimal live load models for design and rating of bridges using reliability-based design optimization (RBDO) methodology, such that target reliability levels for bridge girders subjected to Michigan traffic loads can be consistently met. Traffic data from 20 high-fidelity weigh-in-motion (WIM) sites collected over a two-year period across Michigan will be used for statistical analysis. From the filtered data, load effects are generated for a series of hypothetical bridges considering spans from 20 to 200 ft. and girder spacings from 6 to 12 ft. Simple moments and shears, for both single lane and two lane live load effects, are considered. Based on the load effect data generated from the WIM vehicle configurations, load effects are probabilistically projected to 5 years (for rating) and 75 years (for design) to obtain estimates for the maximum load effect statistics. An extreme type I projection will be considered. Optimal design and rating models are developed with a reliability-based optimization process using a genetic algorithm such that discrepancies in bridge structure reliability index are minimized

Railway bridge structural health monitoring and fault detection: state-of-the-art methods and future challenges

Railway importance in the transportation industry is increasing continuously, due to the growing demand of both passenger travel and transportation of goods. However, more than 35% of the 300,000 railway bridges across Europe are over 100-years old, and their reliability directly impacts the reliability of the railway network. This increased demand may lead to higher risk associated with their unexpected failures, resulting safety hazards to passengers and increased whole life cycle cost of the asset. Consequently, one of the most important aspects of evaluation of the reliability of the overall railway transport system is bridge structural health monitoring, which can monitor the health state of the bridge by allowing an early detection of failures. Therefore, a fast, safe and cost-effective recovery of the optimal health state of the bridge, where the levels of element degradation or failure are maintained efficiently, can be achieved. In this article, after an introduction to the desired features of structural health monitoring, a review of the most commonly adopted bridge fault detection methods is presented. Mainly, the analysis focuses on model-based finite element updating strategies, non-model-based (data-driven) fault detection methods, such as artificial neural network, and Bayesian belief network–based structural health monitoring methods. A comparative study, which aims to discuss and compare the performance of the reviewed types of structural health monitoring methods, is then presented by analysing a short-span steel structure of a railway bridge. Opportunities and future challenges of the fault detection methods of railway bridges are highlighted

Three decades of statistical pattern recognition paradigm for SHM of bridges

This is the author accepted manuscript. The final version is available from SAGE Publications via the DOI in this recordBridges play a crucial role in modern societies, regardless of their culture, geographical location, or economic development. The safest, economical, and most resilient bridges are those that are well managed and maintained. In the last three decades, structural health monitoring (SHM) has been a promising tool in management activities of bridges as potentially it permits one to perform condition assessment to reduce uncertainty in the planning and designing of maintenance activities as well as to increase the service performance and safety of operation. The general idea has been the transformation of massive data obtained from monitoring systems and numerical models into meaningful information. To deal with large amounts of data and perform the damage identification automatically, SHM has been cast in the context of the statistical pattern recognition (SPR) paradigm, where machine learning plays an important role. Meanwhile, recent technologies have unveiled alternative sensing opportunities and new perspectives to manage and observe the response of bridges, but it is widely recognized that bridge SHM is not yet fully capable of producing reliable global information on the presence of damage. While there have been multiple review studies published on SHM and vibration-based structural damage detection for wider scopes, there have not been so many reviews on SHM of bridges in the context of the SPR paradigm. Besides, some of those reviews become obsolete quite fast, and they are usually biased towards applications falling outside of bridge engineering. Therefore, the main goal of this article is to summarize the concept of SHM and point out key developments in research and applications of the SPR paradigm observed in bridges in the last three decades, including developments in sensing technology and data analysis, and to identify current and future trends to promote more coordinated and interdisciplinary research in the SHM of bridges