scholarly journals Research on Comprehensive Multi-Infrastructure Optimization in Transportation Asset Management: The Case of Roads and Bridges

2019 ◽  
Vol 11 (16) ◽  
pp. 4430 ◽  
Author(s):  
Zhang Chen ◽  
Yuanlu Liang ◽  
Yangyang Wu ◽  
Lijun Sun
Keyword(s):  
Asset Management ◽  
Optimization Model ◽  
Optimization Technique ◽  
Infrastructure Management ◽  
Transportation Assets ◽  
Maintenance And Rehabilitation ◽  
Optimum Order ◽  
Transportation Asset Management ◽  
Order Algorithm

Optimization is the core of transportation asset management, but current optimization approaches are still in the stage of single infrastructure management, which seriously hinders the development and application of transportation asset management. This paper establishes a comprehensive multi-infrastructure optimization model for transportation assets consisting of roads and bridges, which is aimed at achieving the goal of transportation asset comfort, integrity, and security, taking budget funds as constraint conditions, and applying the optimization technique of goal programming and integer programming. An interactive fuzzy linear-weighted optimum-order algorithm is presented to solve the comprehensive optimization model. Finally, the comprehensive multi-infrastructure optimization model and algorithm are verified to be effective by practical data in a case study. The results indicate that the model and algorithm can provide a satisfactory and reasonable maintenance and rehabilitation schedule for transportation asset management agencies.

Machine learning and optimization-based modeling for asset management: a case study

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Andrés Muñoz-Villamizar ◽  
Carlos Yohan Rafavy ◽  
Justin Casey
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Asset Allocation ◽  
Asset Management ◽  
Optimization Model ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Content Type ◽  
Demand Prediction

PurposeThis research is inspired by a real case study from a pump rental business company across the US. The company was looking to increase the utilization of its rental assets while, at the same time, keeping the cost of fleet mobilization as efficient as possible. However, decisions for asset movement between branches were largely arranged between individual branch managers on an as-needed basis.Design/methodology/approachThe authors propose an improvement for the company's asset management practice by modeling an integrated decision tool which involves evaluation of several machine learning algorithms for demand prediction and mathematical optimization for a centrally-planned asset allocation.FindingsThe authors found that a feed-forward neural network (FNN) model with single hidden layer is the best performing predictor for the company's intermittent product demand and the optimization model is proven to prescribe the most efficient asset allocation given the demand prediction from FNN model.Practical implicationsThe implementation of this new tool will close the gap between the company's current and desired future level of operational performance and consequently increase its competitivenessOriginality/valueThe results show a superior prediction performance by a feed-forward neural network model and an efficient allocation decision prescribed by the optimization model.

scholarly journals Civil Infrastructure Management Models for the Connected and Automated Vehicles Technology

2019 ◽  
Vol 4 (3) ◽  
pp. 49 ◽  
Author(s):  
John O. Sobanjo
Keyword(s):  
Asset Management ◽  
Civil Infrastructure ◽  
Infrastructure Management ◽  
Automated Vehicles ◽  
Private Industry ◽  
User Costs ◽  
Replacement Model ◽  
Infrastructure System ◽  
Transportation Asset Management ◽  
Age Replacement

The new concept of Connected and Automated Vehicles (CAVs) necessitates a need to review the approach of managing the existing civil infrastructure system (highways, bridges, sign structures, etc.). This paper provides a basic introduction to the CAV concept, assesses the infrastructure requirements for CAVs, and identifies the appropriateness of the existing infrastructure, and needs, in terms of the condition assessment and deterioration modeling. With focus on the Vehicle-to-Infrastructure (V2I) requirements for CAVs, the main elements required on the infrastructure are the Roadside Units (RSUs), which are primarily for communication; they are similar to non-structural transportation assets, such as traffic signals, signs, etc. The ongoing pertinent efforts of agencies and the private industry are reviewed, including the V2I Deployment Coalition (American Association of State Transportation Officials (AASHTO), the Institute of Transportation Engineers (ITE), and the Intelligent Transportation Society of America (ITS America)). Current methods of transportation asset management, particularly, of non-structural elements, are also reviewed. Two reliability-based models were developed and demonstrated for the deterioration of RSUs, including the age replacement model, and a combined survivor function considering the vulnerability of the CAV elements to natural hazards, such as the hurricanes. The paper also discusses the implications of the CAV technology on traffic models, particularly, how it affects user costs’ computations.

Maintenance Management of Railway Bridges and Tunnels

2019 ◽  
Author(s):  
Ana Isabel Silva
Keyword(s):  
Asset Management ◽  
Great Majority ◽  
Management Plan ◽  
Infrastructure Management ◽  
Maintenance Strategy ◽  
Management Activity ◽  
Railway Bridges ◽  
Railway Infrastructure ◽  
Capacity Level ◽  
Maintenance And Rehabilitation

<p>The Portuguese Railway Infrastructure Manager Company (IP) has a long tradition in inspection, maintenance and rehabilitation of railway bridges and tunnels in Portugal.</p><p>The origin of the construction of most of the Tunnels is associated to the date of execution of the railway lines, reason why the great majority of the tunnels existing in the network are centenarian structures. On the other hand, the majority of the bridges in exploration are more than 100 years old. These situations require permanent monitoring, as well as the development of preventive and corrective actions.</p><p>Only an adequate maintenance strategy has ensured normal service conditions in centenary structures, maintaining them at the expected capacity level and ensuring that rail traffic operates unrestrictedly and in expected comfort and speeds.</p><p>The existence of a well-founded Asset Management Plan is extremely important for the infrastructure management activity. The ability to develop an articulated set of actions and costs based on the actual needs of the assets, with the corresponding programming and expected performances with their realization, is fundamental for the infrastructure manager.</p><p>The main objective of this paper is to describe the maintenance strategy followed by IP, and to present some experience in rehabilitation and maintenance works.</p>

Establishing optimal project-level strategies for pavement maintenance and rehabilitation – A framework and case study

2012 ◽  
Vol 44 (5) ◽  
pp. 565-589 ◽  
Author(s):  
Muhammad Irfan ◽  
Muhammad Bilal Khurshid ◽  
Qiang Bai ◽  
Samuel Labi ◽  
Thomas L. Morin
Keyword(s):  
Pavement Maintenance ◽  
Maintenance And Rehabilitation ◽  
Project Level

scholarly journals Load Frequency Control Using Hybrid Intelligent Optimization Technique for Multi-Source Power Systems

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1581
Author(s):  
Deepak Kumar Gupta ◽  
Amitkumar V. Jha ◽  
Bhargav Appasani ◽  
Avireni Srinivasulu ◽  
Nicu Bizon ◽  
...  
Keyword(s):  
Power Plant ◽  
Power System ◽  
Frequency Control ◽  
Optimization Technique ◽  
Load Frequency Control ◽  
Frequency Deviation ◽  
Source Power ◽  
Load Frequency ◽  
Tie Line

The automatic load frequency control for multi-area power systems has been a challenging task for power system engineers. The complexity of this task further increases with the incorporation of multiple sources of power generation. For multi-source power system, this paper presents a new heuristic-based hybrid optimization technique to achieve the objective of automatic load frequency control. In particular, the proposed optimization technique regulates the frequency deviation and the tie-line power in multi-source power system. The proposed optimization technique uses the main features of three different optimization techniques, namely, the Firefly Algorithm (FA), the Particle Swarm Optimization (PSO), and the Gravitational Search Algorithm (GSA). The proposed algorithm was used to tune the parameters of a Proportional Integral Derivative (PID) controller to achieve the automatic load frequency control of the multi-source power system. The integral time absolute error was used as the objective function. Moreover, the controller was also tuned to ensure that the tie-line power and the frequency of the multi-source power system were within the acceptable limits. A two-area power system was designed using MATLAB-Simulink tool, consisting of three types of power sources, viz., thermal power plant, hydro power plant, and gas-turbine power plant. The overall efficacy of the proposed algorithm was tested for two different case studies. In the first case study, both the areas were subjected to a load increment of 0.01 p.u. In the second case, the two areas were subjected to different load increments of 0.03 p.u and 0.02 p.u, respectively. Furthermore, the settling time and the peak overshoot were considered to measure the effect on the frequency deviation and on the tie-line response. For the first case study, the settling times for the frequency deviation in area-1, the frequency deviation in area-2, and the tie-line power flow were 8.5 s, 5.5 s, and 3.0 s, respectively. In comparison, these values were 8.7 s, 6.1 s, and 5.5 s, using PSO; 8.7 s, 7.2 s, and 6.5 s, using FA; and 9.0 s, 8.0 s, and 11.0 s using GSA. Similarly, for case study II, these values were: 5.5 s, 5.6 s, and 5.1 s, using the proposed algorithm; 6.2 s, 6.3 s, and 5.3 s, using PSO; 7.0 s, 6.5 s, and 10.0 s, using FA; and 8.5 s, 7.5 s, and 12.0 s, using GSA. Thus, the proposed algorithm performed better than the other techniques.

Sign in / Sign up

Export Citation Format

Share Document