Stochastic Analysis of Network-Level Bridge Maintenance Needs Using Latin Hypercube Sampling

Politis, Stefanos S; Zhang, Zhanmin; Han, Zhe; Hasenbein, John J.; Arellano, M. Carmen Ramı́rez de

doi:10.1061/ajrua6.0001099

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…The iterative characteristics of metaheuristic algorithms are taken into account. Note that this method is different from Latin hypercube sampling [41] and diagonally uniform initialization; details will be clarified later. The experimental results show that the proposed DLU is more effective than other commonly used initialization methods and is also suitable for most metaheuristic algorithms.…”

Section: With Prior Knowledgementioning

confidence: 99%

Improved Initialization Method for Metaheuristic Algorithms: A Novel Search Space View

Bai

Gao

2021

IEEE Access

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As an essential step of metaheuristic optimizers, initialization seriously affects the convergence speed and solution accuracy. The main motivation of the state-of-the-art initialization method is to generate a small initial population to cover the search space as much as possible uniformly. However, these approaches have suffered from the curse of dimensionality, high computational cost, and sensitivity to parameters, which ultimately reduce the algorithm's convergence speed. In this paper, a new initialization technique named diagonal linear uniform initialization (DLU) is proposed, which follows a novel search view, i.e., adopting the diagonal subspace sampling instead of the whole space. By considering the algorithm's update mechanism, the improved sampling method dramatically improves the convergence speed and solution accuracy of metaheuristic algorithms. Compared with the other eight widely used initialization strategies, the differential evolution (DE) algorithm with DLU obtains the best performance in search accuracy and convergence speed. In the extension experiments, results show that the DLU is still effective for three swarm-based algorithms: particle swarm optimization (PSO), cuckoo search (CS), and artificial bee colony (ABC). Especially for the multi-objective problem, the DLU still demonstrates its powerful performance compared with other strategies.

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Section: With Prior Knowledgementioning

confidence: 99%

Improved Initialization Method for Metaheuristic Algorithms: A Novel Search Space View

Bai

Gao

2021

IEEE Access

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“…Latin hypercube sampling [19] is employed in this article. It is a kind of stratified sampling [20], which can simulate the distribution of problems with fewer points.…”

Section: B Collision Detectionmentioning

confidence: 99%

“…The heuristic function f (x) is shown in Eq. (19), and the distance is defined as an Euclidean distance [22] between two points.…”

Section: B Collision Detectionmentioning

confidence: 99%

Research on the Dynamic Path Planning of Manipulators Based on a Grid-Local Probability Road Map Method

et al. 2021

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A Grid-Local Probability Road Map (PRM) method was proposed for the path planning of manipulators in dynamic environments. Based on the idea of boundary discretization, a double-grid model was built to obtain a mapping from dynamic obstacles to configuration space. The collision detection was simplified as a data indexing process to improve its efficiency. Times of collision detections were reduced by employing local programming strategies and the stratified sampling method. Moreover, the validity of sampling was increased. Taking the PUMA560 manipulator as a research object, the simulation experiments show that the time consumption of the proposed simplified collision-detection algorithm is about 14% of that of the standard one, and the stratified sampling is beneficial to the generation of probability maps compared with simple random sampling method. The simulation experiment of the static path planning shows that the proposed algorithm consumes an average of 10ms, which is superior to the comparison algorithm and has high efficiency and real-time performance. The simulation experiment of the dynamic path planning shows that the proposed algorithm consumes an average of 7ms per step, which is better than the comparison algorithm. The proposed algorithm can adjust the global path in real time to avoid obstacles as the environment changes. The algorithm mentioned has been proved to be efficient.INDEX TERMS Dynamic environment, double grid model, probability road map, path planning.

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“…The value of a method recognizing the influence of multiple possible futures which we refer to here as scenarios was demonstrated by Politis et al (2021) and Pinto and Guilford (2019). Recent experience with the COVID-19 pandemic shows how easily "business as usual" forecasts can radically change, necessitating an approach that is adaptive to emergent and future conditions.…”

Section: Introductionmentioning

confidence: 99%

Enterprise risk management for automation in correctional facilities with pandemic and other stressors

et al. 2022

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Real-time tracking of tool and equipment inventories is a critical function of many organizations and sectors. For prisons and correctional facilities, tracking and monitoring of assets such as cookware, hardware, keys, janitorial equipment, vocational/technical specialty tools, etc., is essential for safety, security, trust, efficiency, education, etc. The performance of automated systems for this purpose can be diminished by a variety of emergent and future sociotechnical factors alone and in combination. This article introduces a methodology for contractor evaluation and selection in acquisition of innovative asset management systems, with an emphasis on evolving system requirements under uncertainty. The methodology features a scenariobased preferences analysis of emergent and future conditions that are disruptive to the performance of the asset-control system. The conditions are across technologies, operating environments, regulations, workforce behaviors, offender behaviors, prices and markets, organizations, cyber threats, etc. The methodology addresses the influence and interaction of the conditions to disrupt system priorities. Examples include: (i) infectious disease disrupting priorities among requirements and (ii) radio-frequency identification (RFID) and wireless-technology innovations disrupting priorities among stakeholders. The combinations of conditions that most and least matter for the system acquisition are characterized. The methodology constitutes a risk register for monitoring sources of risk to project performance, schedule, and cost throughout the system lifecycle. The results will be of interest to both practitioners and scholars engaged in systems acquisition as the pandemic interacts with other factors to affect risk, uncertainty, and resilience of organizational missions and operations.

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Stochastic Analysis of Network-Level Bridge Maintenance Needs Using Latin Hypercube Sampling

Cited by 5 publications

References 20 publications

Improved Initialization Method for Metaheuristic Algorithms: A Novel Search Space View

Improved Initialization Method for Metaheuristic Algorithms: A Novel Search Space View

Research on the Dynamic Path Planning of Manipulators Based on a Grid-Local Probability Road Map Method

Enterprise risk management for automation in correctional facilities with pandemic and other stressors

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