• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.10a
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• pp.159-165
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• 2000

A new state evaluation approach for structural safety is presented in this study. To reduce the subjectivity of the view and judgement of each expert founded on a limited body of knowledge in cognitive and inferential process of safety assessment, we introduced inductive learning method in AI. Inductive learning derives generalization from experiences. Decision tree induction algorithm analyzes the domain knowledge, produce rules via decision trees and then allow us to determine the classification of an object from case examples. The training set of state evaluation is constructed according to the selected attributes from working reports of RC bridge girders.

• Computers and Concrete
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• v.33 no.6
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• pp.643-658
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• 2024

To date, shell-solid and fibre element model analysis are the most commonly used methods to investigate the seismic performance of concrete-filled steel tube (CFST) bridge piers. However, most existing research does not consider the loss of bearing capacity caused by the fracture of the outer steel tube. To fill this knowledge gap, a refined finite element (FE) model considering the ductile damage of steel tubes and the behaviour of infilled concrete with cracks is established and verified against experimental results of unidirectional, bidirectional cyclic loading tests and pseudo-dynamic loading tests. In addition, a parametric study is conducted to investigate the seismic performance of CFST bridge piers with different concrete strength, steel strength, axial compression ratio, slenderness ratio and infilled concrete height using the proposed model. The validation shows that the proposed refined FE model can effectively simulate the residual displacement of CFST bridge piers subjected to highintensity earthquakes. The parametric analysis indicates that CFST piers hold sufficient strength reserves and sound deformation capacity and, thus, possess excellent application prospects for bridge construction in high-intensity areas.

• Computers and Concrete
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• v.14 no.6
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• pp.727-743
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• 2014

The management of existing concrete bridges has become a major social concern in many developed countries due to the large number of bridges exhibiting signs of significant deterioration. This problem has increased the demand for effective maintenance and renewal planning. In order to implement an appropriate management procedure for a structure, a wide array of corrective strategies must be evaluated with respect to not only the condition state of each defect but also safety, economy and sustainability. This paper describes a new performance evaluation system for existing concrete bridges. The system evaluates performance based on load carrying capability and durability from the results of a visual inspection and specification data, and describes the necessity of maintenance. It categorizes all girders and slabs as either unsafe, severe deterioration, moderate deterioration, mild deterioration, or safe. The technique employs an expert system with an appropriate knowledge base in the evaluation. A characteristic feature of the system is the use of neural networks to evaluate the performance and facilitate refinement of the knowledge base. The neural network proposed in the present study has the capability to prevent an inference process and knowledge base from becoming a black box. It is very important that the system is capable of detailing how the performance is calculated since the road network represents a huge investment. The effectiveness of the neural network and machine learning method is verified by comparing diagnostic results by bridge experts.

• Smart Structures and Systems
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• v.24 no.6
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• pp.723-732
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• 2019

Globally road transport networks are subjected to continuous levels of stress from increasing loading and environmental effects. As the most popular mean of transport in the UK the condition of this civil infrastructure is a key indicator of economic growth and productivity. Structural Health Monitoring (SHM) systems can provide a valuable insight to the true condition of our aging infrastructure. In particular, monitoring of the displacement of a bridge structure under live loading can provide an accurate descriptor of bridge condition. In the past B-WIM systems have been used to collect traffic data and hence provide an indicator of bridge condition, however the use of such systems can be restricted by bridge type, assess issues and cost limitations. This research provides a non-contact low cost AI based solution for vehicle classification and associated bridge displacement using computer vision methods. Convolutional neural networks (CNNs) have been adapted to develop the QUBYOLO vehicle classification method from recorded traffic images. This vehicle classification was then accurately related to the corresponding bridge response obtained under live loading using non-contact methods. The successful identification of multiple vehicle types during field testing has shown that QUBYOLO is suitable for the fine-grained vehicle classification required to identify applied load to a bridge structure. The process of displacement analysis and vehicle classification for the purposes of load identification which was used in this research adds to the body of knowledge on the monitoring of existing bridge structures, particularly long span bridges, and establishes the significant potential of computer vision and Deep Learning to provide dependable results on the real response of our infrastructure to existing and potential increased loading.

• Journal of KIBIM
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• v.2 no.2
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• pp.37-45
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• 2012

Modular bridge construction enabling better productivity of design and construction by standardized members and robotic construction becomes an important issue in construction industry. Modular structures needs accurate information delivery between design, fabrication and construction processes. BIM (Building Information Modeling) based parametric modeling was proposed for the modular bridge substructure. Considering ranges of parameters of the modular bridge, fixed value, variables and relations were defined and these parametric models were applied to design, analysis and fabrication. Experience from development of new structures can be embedded in the 3D models, and the models provide efficient and precise knowledge delivery.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.6
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• pp.1413-1420
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• 2015

On the design phase of small size bridge, owing to the lack of related guidelines or standards to determine a superstructure type of bridge, many designers tend to select the type depending on expert's experience and knowledge. Moreover, recently, as types of bridge superstructure become diverse and more conditions need to be considered in the project, the decision makes process become complex. This research covered the selection of a superstructure type of a middle or small size bridge with span length of about 50m, which frequently built for national roadway, selecting type of bridge superstructure more systematic way rather than the existing ways to compare construction methods or to depend on expert's experiences. This study proposes to build a bridge superstructure type selection model using one of the techniques of artificial intelligence techniques SVM by applicability of the model examined through the verification of the actual case.

• Journal of Navigation and Port Research
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• v.38 no.6
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• pp.645-653
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• 2014

A Bridge Navigational Watch Alarm System (hereafter 'BNWAS') is to monitor and detect if an officer of watch(hereafter 'OOW') keeps a sharp lookout on the bridge. The careless lookout of an OOW could lead to marine accidents. For this reason on June 5th, 2009, IMO decided that a ship is equipped with a BNWAS. However, an existing BNWAS gives the OOW a lot of inconvenience and stress in its operation. It requires that the OOW should press reset buttons to confirm their alert watch on the bridge at every three to twelve minute. Many OOWs have complained that at some circumstances they cannot focus on their bridge activities including watch-keeping due to a lots of resetting inputs of BNWAS. Accordingly, IMO has allowed the use of a motion sensor as a resetting device. The motion sensor detects the movements of human body on the bridge and subsequently sends reset signals directly to BNWAS automatically. As a result, OOWs can work uninterrupted. However, some of classification societies and flag authorities have a slightly different stance on the use of motion sensor as a resetting method for BNWAS. The reason is that the motion sensor may trigger false reset signals caused by the motion of objects on the bridge, especially a slight movement such as toss and turn of human body which can extend the period of careless watch. As a basic study to minimize the false reset signals, this paper proposes a simple configuration of BNWAS, which consists of only three motion sensors associated with 'AND' and 'OR' logic gates. Additionally, several considerations are also proposed for the implementation of motion sensors. This study found that the proposed configuration which consists of three motion sensors is better than an existing one by reducing false reset signals caused by a slight movement of human body in one's sleep. The proposed configuration in this paper filters false reset signals and is simple to be implemented on existing vessels. In addition, it can be easily installed just by a basic electrical knowledge.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2011.11a
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• pp.13-15
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• 2011

This report represents analysis of the maritime casualty in terms of Bridge Resource Management. We evaluate the attitudes and knowledge of bridge officer regarding human factors issues that have been identified as causal to mishaps in high-risk situations. So to reduce human errors our goal is to establish effective officer resource management (ORM) program which is based on all subjects for cadets in IMO model course. In harmonization with STCW(The International Convention on Standards of training, Certification and Watch-keeping for Seafarers), as the result, the curriculumss in the maritime education institutions is surveyed to improve our education system and then reduce the human errors by mariners at sea.

• Structural Engineering and Mechanics
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• v.31 no.2
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• pp.137-152
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• 2009

Mechanic behavior of Y-shape thin-walled box girder bridge structure is complex, so one can not exactly hold the mechanical behavior of the Y-shape thin-walled box girder bridge structure through general calculation theory and analytical method. To hold the mechanical behavior better, based on elementary beam theory, by increasing the degree of freedom analytical method, taking account of restrained torsiondistortion angledistortion warp and shearing lag effect at the same time, authors obtain a thin-walled box beam analytical element of 10 degrees of freedom of every node, derive stiffness matrix of the element, and code a finite element procedure. In addition, authors combine the obtained procedure with spatial grillage analytical method, meanwhile, they build a new analytical method that is the spatial thin-walled box girder element grillage analysis method. In order to validate the precision of the obtained analysis method, authors analyze a type Y-shape thin-walled box girder bridge structure according to the elementary beam theory analytical method, the shell theory analytical method and the spatial thin-walled box girder element grillage analysis method respectively. At last, authors test a type Y-shape thin-walled box girder bridge structure. Comparisons of the results of theory analysis with the experimental text show that the spatial thin-walled box girder element grillage analysis method is simple and exact. The research results are helpful for the knowledge of the mechanics property of these Y-shape thin-walled box girder bridge structures.

• Earthquakes and Structures
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• v.13 no.4
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• pp.353-363
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• 2017

The present study considers a multi-span continuous bridge, isolated by lead rubber bearing (LRB). Dynamic soilstructure interaction (SSI) is modelled with the help of a simplified, sway-rocking model for different types of soil. It is well understood from the literature that SSI influences the structural responses and the isolator performance. However, the abovementioned effect of SSI also depends on the earthquake ground motion properties. It is very important to understand how the interaction between soil and structure varies with the earthquake ground motion characteristics but, as far as the knowledge of the authors go, no study has been carried out to investigate this effect. Therefore, the objectives of the present study are to investigate the influence of earthquake ground motion characteristics on: (a) the responses of a multi span bridge (isolated and non-isolated), (b) the performance of the isolator and, most importantly, (c) the soil-structure interaction. Statistical analyses are conducted by considering 14 earthquakes which are selected in such a way that they can be categorized into three frequency content groups according to their peak ground acceleration to peak ground velocity (PGA/PGV) ratio. Lumped mass model of the bridge is developed and time history analyses are carried out by solving the governing equations of motion in the state space form. The performance of the isolator is studied by comparing the responses of the bridge with those of the corresponding uncontrolled bridge (i.e., non-isolated bridge). On studying the effect of earthquake motions, it is observed that the earthquake ground motion characteristics affect the interaction between soil and structure in such a way that the responses decrease with increase in frequency content of the earthquake for all the types of soil considered. The reverse phenomenon is observed in case of the isolator performance where the control efficiencies increase with frequency content of earthquake.