Decision Making Methods and Applications in Civil Engineering

Antuchevičienė, Jurgita; Kala, Zdeněk; Marzouk, Mohamed; Vaidogas, Egidijus Rytas

doi:10.1155/2015/160569

Cited by 21 publications

(17 citation statements)

References 11 publications

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“…AHP model-based MCDM methodology has successfully been applied for the sustainability assessment in civil engineering [8,9]. Antucheviciene et al [10] have presented a review of decisionmaking methods and applications in civil engineering. Monghasemi et al [11] have developed a new MCDM model to optimize the time-cost-quality in construction projects.…”

Section: Introductionmentioning

confidence: 99%

Decision Support Model for Design of High‐Performance Concrete Mixtures Using Two‐Phase AHP‐TOPSIS Approach

Ahmed

Qureshi

Mallick

et al. 2019

Advances in Civil Engineering

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Concrete mix design is the science to obtain concrete proportions of cement, water, and aggregate, based on the particular concrete design method and their mix design parameters. However, the suitability of concrete proportion for high-performance concrete depends on resulting mix factors, namely, water, cement, fine aggregate, and coarse aggregate ratios. This paper implements the multicriteria decision-making techniques (MCDM) for ranking concrete mix factors and representative mix design methods. The study presents a framework to identify critical mix factors found from the concrete mix design methods for high-performance concrete using the two-phase AHP and TOPSIS approach. Three methods of concrete mix design, namely, American Concrete Institute (ACI) mix design method, Department of Energy (DOE) method, and Fineness Modulus (FM) method, are considered for ranking mix design methods and the resulting mix factors. Three hierarchy levels, having three criteria and seven subcriteria, and three alternatives are considered. The present research is attempted to provide MCDM framework to rank the concrete mix guidelines for any given environment such as concrete under sulphate and chloride attack and for evolving the performance-based concrete mix design techniques. Sensitivity and validation analysis is also provided to demonstrate the effectiveness of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Decision Support Model for Design of High‐Performance Concrete Mixtures Using Two‐Phase AHP‐TOPSIS Approach

Ahmed

Qureshi

Mallick

et al. 2019

Advances in Civil Engineering

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“…Recent advances and the general accessibility of information technologies and computing techniques give rise to assumptions concerning the wider use of the probabilistic assessment of the reliability of structures through the use of simulation methods [1][2][3][4][5]. Much attention should be paid to using the probabilistic approach in an analysis of the reliability of structures [6,7].…”

Section: Introductionmentioning

confidence: 99%

Deterministic and Probabilistic Analysis of the Random Torsional Effects of the Seismic Resistance of Symmetrical Tall Buildings

Králik¹

2018

Zeszyty Naukowe Politechniki Częstochowskiej. Budownictwo

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“…In business, it is possible to identify a strategic [1] or operational [2] approach or a more particularly focused approach for suppliers [3]; in the engineering field, it is possible to identify cases regarding manufacturing conditions [4], product design [5], or aspects relating to civil engineering [6]. In the context of the current market, in which delivery times are continually reduced and, more importantly, responses to orders are increasingly immediate, the production response in the industrial environment is faster, and quality and time affect both the complexity and the flexibility of the system [7].…”

Section: Introductionmentioning

confidence: 99%

An Approach to Integrating Tactical Decision-Making in Industrial Maintenance Balance Scorecards Using Principal Components Analysis and Machine Learning

2017

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The uncertainty of demand has led production systems to become increasingly complex; this can affect the availability of the machines and thus their maintenance. Therefore, it is necessary to adequately manage the information that facilitates decisionmaking. This paper presents a system for making decisions related to the design of customized maintenance plans in a production plant. This paper addresses this tactical goal and aims to provide greater knowledge and better predictions by projecting reliable behavior in the medium-term, integrating this new functionality into classic Balance Scorecards, and making it possible to extend their current measuring function to a new aptitude: predicting evolution based on historical data. In the proposed Custom Balance Scorecard design, an exploratory data phase is integrated with another analysis and prediction phase using Principal Component Analysis algorithms and Machine Learning that uses Artificial Neural Network algorithms. This new extension allows better control over the maintenance function of an industrial plant in the medium-term with a yearly horizon taken over monthly intervals which allows the measurement of the indicators of strategic productive areas and the discovery of hidden behavior patterns in work orders. In addition, this extension enables the prediction of indicator outcomes such as overall equipment efficiency and mean time to failure.

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Decision Making Methods and Applications in Civil Engineering

Cited by 21 publications

References 11 publications

Decision Support Model for Design of High‐Performance Concrete Mixtures Using Two‐Phase AHP‐TOPSIS Approach

Decision Support Model for Design of High‐Performance Concrete Mixtures Using Two‐Phase AHP‐TOPSIS Approach

Deterministic and Probabilistic Analysis of the Random Torsional Effects of the Seismic Resistance of Symmetrical Tall Buildings

An Approach to Integrating Tactical Decision-Making in Industrial Maintenance Balance Scorecards Using Principal Components Analysis and Machine Learning

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