Tunneling parameters optimization based on multi-objective differential evolution algorithm

Wang, Hongyuan; Wang, Jingcheng; Zhao, Yaqi; Xu, Haotian

doi:10.1007/s00500-020-05392-8

Cited by 17 publications

(3 citation statements)

References 45 publications

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“…During the construction process, construction managers cannot pursue low costs and only ensure that the project quality meets the minimum standards specified by relevant laws, regulations, and investors. This paper invites experts to rate each operation mode on a percentage system and determine the weight of each individual process in the entire engineering project [4][5]. The quality model constructed as shown in formula (3):…”

Section: Engineering Project Quality Modelmentioning

confidence: 99%

Multi-objective Optimization of Construction Projects Based on Ant Colony Algorithm

Chen

2024

Atlantis Highlights in Engineering

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To understand the multi-objective optimization of construction projects, a multi-objective optimization study of construction projects based on ant colony algorithm has been proposed. This paper first proposes a multi colony ant colony particle swarm fusion algorithm, and conducts empirical research on the algorithm based on a subway project example. Based on the analysis of the solution effectiveness, it is concluded that this algorithm obtains more Pareto solution sets than the basic ant colony algorithm when solving multi-objective joint optimization problems, and can be applied to integrated management of construction projects.

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Section: Engineering Project Quality Modelmentioning

confidence: 99%

Multi-objective Optimization of Construction Projects Based on Ant Colony Algorithm

Chen

2024

Atlantis Highlights in Engineering

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“…The total torque of the motor, the power of the cutter head, the total power of the ( Huang et al, 2022 ) motor, the total current of the motor, advance speed, cutting head pressure, TF, PR, cutting head rotation speed, and field penetration index Swarm intelligence optimization algorithm (Ant Lion Optimizer (ALO), Loin Swarm Optimization (LSO), Seagull Optimization Algorithm (SOA), and Extreme Learning Machine (ELM) model UCS, BTS, RMR, RQD, q, TF, RPM ( C. In addition to the aforementioned cases, it is evident that calculating and predicting the excavator's advance rate is important to determine the excavator's penetration rate parameter (Wang, Wang, Zhao, & Xu, 2021). Many researchers have used the advance rate of the excavator to estimate other parameters affecting the performance of the TBM excavator.…”

Section: Selectkbest Algorithmmentioning

confidence: 99%

Optimizing the gene expression algorithm using the whale algorithm to predict the penetration rate of the tunnel boring machine

Noorian-Bidgoli

2023

Preprint

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Tunnel projects entail high levels of uncertainty due to vague geological conditions and the complexity of the mechanized tunneling process. The effectiveness of the tunnel-boring machine (TBM) is indispensable for the completion of any mechanical tunneling project. The capital costs and schedule of tunnel excavation may be reduced by precisely predicting TBM performance, particularly under certain rock mass conditions. This study attempts to present an optimized model of the gene expression algorithm using the whale optimization algorithm. The TBM drilling machine's penetration rate is a performance metric to provide a precise prediction target for the suggested models. Site surveys for the Qom metro line A project and numerous lab tests on the gathered rock samples led to the developing of a test database with 5742 data sets for modeling purposes. A combination of rock and machine characteristics having the largest impact on the drilling machine penetration rate was utilized to create intelligent models of drilling machine penetration rate relying on training and test patterns. A total of 7 parameters were used as input parameters. The prediction accuracy of the created models was also assessed and compared using several statistical indicators, including variance calculation, coefficient of determination, and root mean square error. Depending on the simulation results and the estimated values of the indices, the correlation coefficient values in the gene expression model and the model optimized by the whale algorithm were calculated as 0.79 and 0.91, respectively. This indicates the significant performance of the whale algorithm in optimizing the results of the gene expression algorithm aimed at predicting the penetration rate of the TBM.

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“…Swarm intelligence algorithms have been applied in the field of tunnel optimization. Wang H et al proposed a differential evolution-based multi-objective genetic algorithm to optimize the feed speed and cutter speed of the tunnel boring machine under different geological conditions [ 22 ]. Moreover, the results show that the optimization results are better than those of the manual experience.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Driving Parameters of the Jumbo Drill Efficiently with XGBoost-DRWIACO Framework: Applied to Increase the Feed Speed

Guo,

Lin,

et al. 2024

Sensors

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The jumbo drill is a commonly used driving equipment in tunnel engineering. One of the key decision-making issues for reducing tunnel construction costs is to optimize the main driving parameters to increase the feed speed of the jumbo drill. The optimization of the driving parameters is supposed to meet the requirements of high reliability and efficiency due to the high risk and complex working conditions in tunnel engineering. The flaws of the existing optimization algorithms for driving parameter optimization lie in the low accuracy of the evaluation functions under complex working conditions and the low efficiency of the algorithms. To address the above problems, a driving parameter optimization method based on the XGBoost-DRWIACO framework with high accuracy and efficiency is proposed. A data-driven prediction model for feed speed based on XGBoost is established as the evaluation function, which has high accuracy under complex working conditions and ensures the high reliability of the optimized results. Meanwhile, an improved ant colony algorithm based on dimension reduction while iterating strategy (DRWIACO) is proposed. DRWIACO is supposed to improve efficiency by resolving inefficient iterations of the ant colony algorithm (ACO), which is manifested as falling into local optimum, converging slowly and converging with a slight fluctuation in a certain dimension. Experimental results show that the error by the proposed framework is less than 10%, and the efficiency is increased by over 30% compared with the comparison methods, which meets the requirements of high reliability and efficiency for tunnel construction. More importantly, the construction cost is reduced by 19% compared with the actual feed speed, which improves the economic benefits.

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Tunneling parameters optimization based on multi-objective differential evolution algorithm

Cited by 17 publications

References 45 publications

Multi-objective Optimization of Construction Projects Based on Ant Colony Algorithm

Multi-objective Optimization of Construction Projects Based on Ant Colony Algorithm

Optimizing the gene expression algorithm using the whale algorithm to predict the penetration rate of the tunnel boring machine

Optimizing Driving Parameters of the Jumbo Drill Efficiently with XGBoost-DRWIACO Framework: Applied to Increase the Feed Speed

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