A Practical Decision-Support System for Bridge Management Based on JAVA Techniques

A Spatial Data Structure for the 3D Graphical Facility Management System

To process enormous 3D data, we have designed a VLIW (Very Long Instruction Word) processor called FLOVA (Floating-Point VLIW Architecture) exploiting the ILP (Instruction-Level Parallelism) in 3D programs. This paper presents FGA (FLOVA Geometry Accelerator) that is the 3D graphics system and it almost removes the time required to process in the geometry stage. We have developed the 3D graphics library, FGA-GL, to supports the FGA system. The deferred primitive rendering algorithm of FGA-GL enables the geometry processing of the primitive data to be done concurrently with the host job such as primitive data management or game play. FGA improves the average performance of 3D graphics system by 2.5-3.0.

Better characterization of the fertilizer spreading process, especially the fertilizer pattern distribution on the ground, requires an accurate measurement of individual particle properties and dynamics. Both 2D and 3D high speed imaging techniques have been developed for this purpose. To maximize the accuracy of the predictions, a specific illumination level is required. This paper describes the development of a high irradiance LED system for high speed motion estimation of fertilizer particles. A spectral sensitivity factor was used to select the optimal LED in relation to the used camera from a range of commercially available high power LEDs. A multiple objective genetic algorithm was used to find the optimal configuration of LEDs resulting in the most homogeneous irradiance in the target area. Simulations were carried out for different lenses and number of LEDs. The chosen configuration resulted in an average irradiance level of 452 W/m2 with coefficient of variation less than 2%. The algorithm proved superior and more flexible to other approaches reported in the literature and can be used for various other applications.

In order to establish a rational bridge management program, it is necessary to develop a cost-effective decision-support system for the maintenance of bridges. In this paper, an attempt is made to develop a bridge management system that can provide practical maintenance plans by using an improved multi-objective genetic algorithm. A group of bridges is analyzed to demonstrate the applicability and efficiency of the proposed method. INTRODUCTIONIn Japan, maintenance work on bridge structures is increasing in volume and importance. The number of structures requiring repair or replacement will drastically increase in the coming ten years. In order to establish a rational maintenance program, the concept of Life-Cycle Cost (LCC) has gained great attention, which minimizes the total cost of whole lives of structures [1]. So far, the authors have developed LCC based bridge maintenance systems for existing concrete bridge structures [2, 3]. Concrete bridges are deteriorating due to the corrosion of reinforcing bars and the neutralization of concrete. Then, it is necessary to achieve an optimal maintenance plan that can provide appropriate methods and times of repair or replacement. However, the optimal maintenance problem is very difficult to solve, because it is one of combinatorial problems with discrete design variables and discontinuous objective functions. Furthermore, the tougher problem becomes , the larger and more complex it is. Although low-cost maintenance plans are desirable for bridge owners it is necessary to consider lifetime constraints when choosing an appropriate maintenance program. For example, the minimization of maintenance cost needs the target safety level and the expected service lifetime to be both prescribed. The predetermination of requirements may affect the possible maintenance plans. Namely, it may be possible to find out a better solution that can largely extend the service life if the safety level can be slightly decreased using the same amount of maintenance cost [4, 5]. In this paper, an attempt is made to improve the bridge management system proposed in [6], by developing an improved multi-objective genetic algorithm. Many alternative 17 ANALYSIS AND COMPUTATION SPECIALTY CONFERENCE th Copyright ASCE 2006 17th Analysis and Computation Conference maintenance plans with different characteristics can be obtained by introducing the concept of multi-objective optimization. Single-objective optimization can provide various solutions by changing the constraints. However, this approach requires enormous computation time. When selecting a practical maintenance plan, it is essential to compare feasible solutions obtained under various conditions. This process is effective for the accountability through the disclosure of information. A new multi-objective genetic algorithm is developed for the bridge management problems that have a lot of constraints. Several numerical examples are presented to demonstrate the applicability and efficiency of the proposed method.

The next generation of telecom networks, the Fifth generation (5G) have started hitting the market end of 2018 and will continue expand worldwide, introducing new features, higher data rates(up to 1Gbps) as well as massive IoT(Internet of Things) and the inclusing of artificial intelligence (AI) and its sub categories like machine learning and deep learning to solve many problem such as the radio network planning problem. While most existing research focuses on the use of deterministic algorithms, or meta-heuristic algorithms with a single objective, this paper exploits the power of genetic algorithm in addition to achieving multiple objective genetic algorithm (MOGA): to minimize cost and interference and to maximize user coverage. The experimental results prove that the use of genetic algorithm with stochastic universal selection is quite efficient in achieving those objectives, as well as, reaching a near optimal solution in a short infinite time.

Bridge management involves all efforts to build, preserve, and operate bridge networks cost-effectively with an objective to deliver the best value for the public tax dollars spent. The dissertation consists of three complementary studies that address both bridge management policies and condition data that contribute to bridge management practices.\nThis dissertation begins with an overview of federal and state government bridge management efforts taken in conjunction with the federal bridge programs in the last 40 years. While the majority of the states have implemented a BMS, the level of implementation is varied, and the overall input from BMSs to network-level decisions remains minimal. Survey findings from 40 states indicate that federal funding eligibility is the major criterion that impacts state-level bridge management decisions. State transportation agencies need federal guidance on areas such as using decision support tools, implementing BMSs, and improving data quality. The findings from the study are useful to both practitioners and policy makers, and identify challenges and needs for bridge management at both federal and state level.\nFollowing the policy study, a statistical comparison of field NBI condition ratings and ratings generated by FHWA's NBI Translator (BMSNBI) algorithm for Iowa bridges is presented. Statistical analysis indicates that the ratings generated by the NBI Translator algorithm are not representative of actual NBI ratings. Results from the research raise questions about the effectiveness of the algorithm.\nFinal study in this dissertation presents a new methodology to predict National Bridge Inventory (NBI) condition ratings from bridge management system (BMS) element condition data, based on Classification and Regression Trees (CART). The proposed methodology achieves significantly better accuracies than other methodologies reported in the literature for the data set used in this study. The CART prediction methodology uses simple and logical conditions of BMS element condition data to predict NBI condition ratings and has potential use for federal and state transportation agencies to summarize bridge condition data.

Multi-objective electricity-gas flow with stochastic dispersion control for air pollutants using two-stage Pareto optimization

The objectives of a bridge manager are to ensure that bridges achieve their design life, that they remain open to traffic continuously and that their risk of failure is always very low. These objectives are to be achieved sustainably and at minimum lifetime cost. The chapter divides the subject into project- and network-level bridge management: the former is concerned with individual bridges, whereas the latter deals with the management of bridge stocks. Project-level management includes such aspects as inspection, testing and deciding maintenance requirements and appropriate prevention and remedial methods and monitoring strategies. Network-level management includes estimating the rate of deterioration, the prediction of future condition using Markov chain models, planning optimal maintenance programmes, prioritising maintenance and assessing the effectiveness of different maintenance strategies. Maintenance affordability, backlogs and long-term plans are also discussed, as are some important techniques used by bridge managers, for example, whole-life costing, probability modelling and sustainability assessment. The chapter concludes with a brief review of recent research on bridge management systems and the results of a survey on the type and functionality of bridge management systems used by local authorities and other bridge owners in the UK.

Sustainability and digitalization are two main topics in many research programs in Europe and worldwide. It seems that bridge managers can largely benefit from the current digital transformation where processes using digital technologies are able to create new or to enhance existing bridge management systems (BMS). The concepts of sustainability and digitalization are very different in essence and, therefore, the tools needed for their fully adoption in the bridge management environment have to be also different in their basis and formulation. For an optimal adoption and development of both topics, also the actual trends in the bridge management policies, as performance-based decision making and adaptive BMS should be considered. This paper analyzes the actual bridge management policies, trying to evaluate their actual level of sustainability and digitalization. It is shown that although in the technical aspects the final goals are not so far, the human factor (educational and organizational aspects) remains as the critical factor for a full and optimal implementation.

During 1979, CERN began to evaluate how interactive computer graphics displays could aid the analysis of high-energy physics experiments at the new Super Proton Synchrotron collider. This work led to PIONS, a 3D graphics system, which features the ability to store and view hierarchical graphics structures in a directed-acyclic-graph database. It is possible to change the attributes of these structures by making selections on nongraphical information also stored in the database. PIONS is implemented as an object-oriented message-passing system based on Small Talk design principles. It supports multiple viewing transformations, logical input devices, and 2D and 3D primitives. The design allows full use to be made of display hardware that provides dynamic 3D picture transformation.

Many tools have been developed to visualize protein and molecular structures. Most high quality protein visualization tools use the OpenGL graphics library as a 3D graphics system. Currently, the performance of recent 3D graphics hardware has rapidly improved. Recent high-performance 3D graphics hardware support Microsoft Direct3D graphics library more than OpenGL and have become very popular in personal computers (PCs). In this paper, a molecular visualization system termed ProteinVista is proposed. ProteinVista is well-designed visualization system using the Microsoft Direct3D graphics library. It provides various visualization styles such as the wireframe, stick, ball and stick, space fill, ribbon, and surface model styles, in addition to display options for 3D visualization. As ProteinVista is optimized for recent 3D graphics hardware platforms and because it uses a geometry instancing technique, its rendering speed is 2.7 times faster compared to other visualization tools.

Multi-objective optimization of a high-speed train head based on the FFD method

‘On the formation of price expectations’ by König et al.

• Comprehensive review of recent advancements in BMS integrating DTs, DL and BD. • Identification and classification of key sources of uncertainty across BMS modules and strategies for their management. • Critical analysis of DT and DL-based frameworks for reliability enhancement and informed decision-making. • Discussion of research gaps and opportunities for integrating advanced technologies into bridge maintenance and monitoring. • Emphasis on future directions toward intelligent, adaptive, and uncertainty-aware bridge management systems Bridge infrastructure, as a critical capital asset and an integral component of transportation networks, is increasingly affected by aging, deterioration, and external damage, potentially compromising its safety, performance, and functionality. These challenges, compounded by the growing demand for infrastructure expansion and limited funding resources, underscore the necessity of adopting efficient bridge management systems to prioritize maintenance and remediation strategies. To address these demands, different Bridge Management Systems (BMSs) have been developed to support operators in maintaining safe operations while optimizing budget allocation and maintenance strategies. Despite advancements in this field, most state-of-the-art research lacks a comprehensive overview of the role of Digital Twins (DTs), Deep learning (DL) and Big Data (BD) integration in enhancing BMS, particularly in managing uncertainties across BMS modules. Therefore, this paper provides a dedicated review of recent research in BMSs, with particular emphasis on the integration of DT and DL-enabled frameworks into bridge management practices and organizes key sources of uncertainty and discusses how emerging technologies can address these aspects in BMSs, which has yet to be comprehensively addressed. The review critically explores current methodologies, highlights challenges and identifies opportunities for uncertainty management in BMSs through the integration of advanced technologies and future trends for more reliable bridge management.

Bridges deteriorate over their life cycles and require continuous maintenance to ensure their structural integrity, and in turn, the safety of the public. Maintaining bridges is a multi‐faceted operation that requires both domain knowledge and analytics techniques over large data sources. Although most existing bridge management systems (BMS) are very efficient at data storage, they are not as effective at providing analytical capabilities or as flexible at supporting different inspection technologies. In this paper, we present a visual analytics system that extends the capability of current BMSs. Based on a nation‐wide survey and our interviews with bridge managers, we designed our system to be customizable so that it can provide interactive exploration, information correlation, and domain‐oriented data analysis. When tested by bridge managers of the U.S. Department of Transportation, we validated that our system provides bridge managers with the necessary features for performing in‐depth analysis of bridges from a variety of perspectives that are in accordance to their typical workflow.