Ground Motion Record Selection using Multi-objective Optimization Algorithms: A Comparative Study

Kaveh, A.; Moghanni, Roya Mahdipou; Javadi, Seyed Mohammad

doi:10.3311/ppci.14354

Cited by 21 publications

(5 citation statements)

References 23 publications

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“…(2) difficulties in attracting and retaining top-quality workers if employment is unstable; (3) disruptions in learning curve effects; and (4) need to maintain the unproductive level of workers on site, which keeps some workers idle during periods of low demand [31]. Besides, when resources from other sources are hired or shared across multiple projects, it is imperative to reduce the resource-utilization timeframe.…”

Section: 𝑃𝐶𝑇 = ∑ 𝑇 𝑖 𝑐𝑝mentioning

confidence: 99%

“…Based on each activity's execution mode, respective values for objective functions are calculated using Eqs. (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11).…”

Section: 𝑃𝐶𝑇 = ∑ 𝑇 𝑖 𝑐𝑝mentioning

confidence: 99%

“…Using these methodologies, decision-makers can identify the best solutions to choose from while maximizing the benefits that can be gained from current resources. There have been many multi-objective algorithms developed for dealing with bi-objective problems like the time-cost tradeoff problem, including nondominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO) [2][3][4][5][6][7]. Several kinds of multi-objective optimization models have been developed incorporating one or more other factors such as quality, safety, environmental impact, and resource moment deviation into bi-objective models.…”

Section: Introductionmentioning

confidence: 99%

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Many-objective optimization for construction project scheduling using non-dominated sorting differential evolution algorithm based on reference points

2021

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Scheduling is considered to be one of the most significant factors in the success of construction projects. In recent years, global construction markets have become increasingly competitive, and the number of project stakeholders has grown significantly. As a result, concurrently pursuing multiple project objectives, such as optimizing the time, cost, resources, environmental impact, safety risks, and quality of a project, is imperative. Several types of research efforts have focused on multiple-objective construction scheduling models to deal with the above mentioned objectives. However, there is still a need to integrate all these objectives in the scheduling process to take into account most aspects of a project. To fill this gap, a manyobjective optimization model regarding time, cost, resource, environmental impact, safety, and quality based on a newly developed many-objective optimization algorithm, Non-dominated Sorting Differential Evolution algorithm based on Reference points (NSDE-R) is presented in this study. To determine the most proper schedule based on decision-makers' priorities, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is merged with the optimization algorithm. The proposed model's applicability is demonstrated employing a case study of a building construction project.

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Section: 𝑃𝐶𝑇 = ∑ 𝑇 𝑖 𝑐𝑝mentioning

confidence: 99%

“…Based on each activity's execution mode, respective values for objective functions are calculated using Eqs. (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11).…”

Section: 𝑃𝐶𝑇 = ∑ 𝑇 𝑖 𝑐𝑝mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Many-objective optimization for construction project scheduling using non-dominated sorting differential evolution algorithm based on reference points

2021

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“…Nowadays, Metaheuristics are used as a powerful tool for engineering prediction problems [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Shear Strength Prediction of FRP-reinforced Concrete Beams Using an Extreme Gradient Boosting Framework

Kaveh

Eslamlou²,

Moghani

2021

Period. Polytech. Civil Eng.

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Despite the importance and accuracy of empirical models, most of the existing models are only accurate on the collected experimental data. Adding new data, or even considering noise or variance in the data leads to loss of model accuracy. The objective of this paper is to alleviate overfitting and develop a more accurate and reliable alternative method using a decision-tree-based ensemble Machine Learning algorithm that uses a gradient boosting framework for the prediction of the ultimate shear strength of FRP-reinforced concrete beams without stirrups. To enhance the robustness of the results, make full use of training samples (without the validation set), and alleviate the randomness in selecting test samples, the K-Fold Cross Validation method is employed. Using a dataset including 205 samples, results show that the extreme gradient boosting framework (XGBoost) providing better prediction. In fact, XGBoost results have higher precision and higher generalization in comparison with the empirical equations, the current design codes of practice, Least Absolute Shrinkage and Selection Operator model (LASSO), and Random Forest model (RF).

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“…Recently, researchers have employed different mathematical and evolutionary algorithms for optimization in civil engineering such as [4][5][6][7][8]. In reliability engineering, Fu and Frangopol [9] proposed a framework to achieve minimum structural weight, highest system reliability, and highest system redundancy.…”

Section: Introductionmentioning

confidence: 99%

Reliability Analysis via an Optimal Covariance Matrix Adaptation Evolution Strategy: Emphasis on Applications in Civil Engineering

Kaveh

Javadi

Moghanni

2020

Period. Polytech. Civil Eng.

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In this paper, a reliability-based optimization approach is applied using a recently proposed CMA-ES with optimal covariance update and storage complexity. Cholesky-CMA-ES gives a significant increase in optimization speed and reduces the runtime complexity of the standard CMA-ES. The reliability index is the shortest distance between the surface of Limit-State Function (LSF) and the origin of the standard normal space. Hence, finding the reliability index can be expressed as a constrained optimization problem. To verify the concept and test the feasibility of this algorithm, several numerical examples consisting of mathematical and highly nonlinear civil engineering problems are investigated. The reliability indexes obtained agree reasonably well with reported values from some existing approximation methods and Monte Carlo simulation.

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Ground Motion Record Selection using Multi-objective Optimization Algorithms: A Comparative Study

Cited by 21 publications

References 23 publications

Many-objective optimization for construction project scheduling using non-dominated sorting differential evolution algorithm based on reference points

Many-objective optimization for construction project scheduling using non-dominated sorting differential evolution algorithm based on reference points

Shear Strength Prediction of FRP-reinforced Concrete Beams Using an Extreme Gradient Boosting Framework

Reliability Analysis via an Optimal Covariance Matrix Adaptation Evolution Strategy: Emphasis on Applications in Civil Engineering

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