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Algorithms
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Simulation Modeling and Optimization Algorithms in Construction
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Simulation Modeling and Optimization Algorithms in Construction

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 3092

Special Issue Editor

Dr. Mohammad Raoufi

E-Mail Website
Guest Editor
School of Engineering, University of Northern British Columbia, Prince George, BC V2N 4Z9, Canada
Interests: construction; simulation; artificial intelligence; safety; productivity; performance; energy solutions; decision making
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite you to submit your latest research in the area of the development of optimization algorithms to this Special Issue, “Simulation Modeling and Optimization Algorithms in Construction”. We are looking for new and innovative algorithms for solving problems in engineering and construction. High-quality papers are sought that address both theoretical and practical issues in improvement in engineering and construction practices using simulation modeling algorithms and artificial intelligence algorithms. Submissions are welcome on both fundamental and theoretical research, as well as professional applications. Potential topics include, but are not limited to, multiagent systems, artificial neural networks, Monte Carlo simulation, qualitative and quantitative modeling, multicriteria decision making, as well as a broad spectrum of professional applications in engineering and construction, such as risk, energy, safety, productivity, and performance.

Dr. Mohammad Raoufi
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • simulation
  • modeling
  • optimization algorithms
  • construction engineering
  • construction management
  • multiagent systems
  • artificial neural networks
  • Monte Carlo simulation
  • qualitative and quantitative modeling
  • multicriteria decision making

Published Papers (2 papers)

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Research

20 pages, 3735 KiB  
Article
Integrated Decision Support Framework of Optimal Scaffolding System for Construction Projects
by Haifeng Jin and Paul M. Goodrum
Algorithms 2023, 16(7), 348; https://doi.org/10.3390/a16070348 - 20 Jul 2023
Cited by 1 | Viewed by 1211
Abstract
Selecting the appropriate temporary facilities is important for reducing cost and improving the productivity and safety of craft professionals in construction projects. However, the manual planning process for scaffolding systems is typically prone to inefficiencies. This paper aims to develop a knowledge-based framework [...] Read more.
Selecting the appropriate temporary facilities is important for reducing cost and improving the productivity and safety of craft professionals in construction projects. However, the manual planning process for scaffolding systems is typically prone to inefficiencies. This paper aims to develop a knowledge-based framework for a scaffolding decision support system for industry. An integrated two-phase system was established, including a technical evaluation module and a knowledge-based module. First, the system identifies feasible scaffolding alternatives from the database through a rule-based algorithm. Second, a knowledge-based module was designed to assess the alternative performance. The framework effectively generated the ranking of scaffolding alternatives, and the top three influential factors were identified, including the site accessibility, protection to workers and health risk. Thus, an application study of an industrial steel project was proffered to validate the effectiveness of the framework. The proposed framework may help decision-making regarding the implementation of temporary facility planning in industry practices. It has wider applicability because it simultaneously considers site conditions, productivity, safety, and financial benefits, and is designed and implemented through a computerized path. The paper contributes to the industry by developing an integrated decision support system for temporary facilities. Additionally, the practical contribution of this research is the provision of an optimized scaffolding planning method that could be utilized as a guide when implementing the decision support system. Full article
(This article belongs to the Special Issue Simulation Modeling and Optimization Algorithms in Construction)
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20 pages, 1917 KiB  
Article
A Corrosion Maintenance Model Using Continuous State Partially Observable Markov Decision Process for Oil and Gas Pipelines
by Ezra Wari, Weihang Zhu and Gino Lim
Algorithms 2023, 16(7), 345; https://doi.org/10.3390/a16070345 - 18 Jul 2023
Viewed by 977
Abstract
This paper proposes a continuous state partially observable Markov decision process (POMDP) model for the corrosion maintenance of oil and gas pipelines. The maintenance operations include complex and extensive activities to detect the corrosion type, determine its severity, predict the deterioration rate, and [...] Read more.
This paper proposes a continuous state partially observable Markov decision process (POMDP) model for the corrosion maintenance of oil and gas pipelines. The maintenance operations include complex and extensive activities to detect the corrosion type, determine its severity, predict the deterioration rate, and plan future inspection (monitoring) schemes and maintenance policy. A POMDP model is formulated as a decision-making support tool to effectively handle partially observed corrosion defect levels. It formulates states as the pipeline’s degradation level using a probability distribution. Inline inspection (ILI) methods estimate the latest state of the pipeline, which also defines the initial state of the optimization process. The set of actions comprises corrosion mitigation operations. The errors associated with the ILI method are used to construct the observation function for the model. The sum of inspection, maintenance operations, and failure costs for a given state and action are formulated as rewards. Numerical experiments are made based on data collected from the literature. The results showed that different policies, whether derived from solvers (theoretical) or determined from practical experience, can be compared to identify the best maintenance alternative using the model. It was also observed that the choice of the solvers is important since they affect the discounted rewards and the run time to obtain them. The model approximates the parameters and uncertainty associated with the propagation of corrosion, proficiency of inspection methods, and implementation of maintenance policies. Overall, it can be applied to improve the maintenance decision-making process for the oil and gas pipeline as it incorporates the stochastic features of the operation. Full article
(This article belongs to the Special Issue Simulation Modeling and Optimization Algorithms in Construction)
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