Optimized prediction models for faulting failure of Jointed Plain concrete pavement using the metaheuristic optimization algorithms

Ehsani, M.; Hamidian, Pouria; Hajikarimi, Pouria; Nejad, Fereidoon Moghadas

doi:10.1016/j.conbuildmat.2022.129948

Cited by 15 publications

(4 citation statements)

References 23 publications

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“…Four feature selection methods were developed by combining Multi-objective PSO (MPSO) with decomposition-based multi-objective evolutionary algorithm. These experiments confirmed that the model had the best performance and could identify 17 input variables that affect faults [10].…”

Section: Related Workmentioning

confidence: 54%

Interior Scene Coloring Design Model Combining Improved K-means and SAA

2024

SCPE

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Due to technological progress and changes in people’s aesthetic standards, traditional design models need to be constantly broken through, seeking more efficient and accurate design methods. Seeking effective design models to improve design efficiency and prediction accuracy is an important task. Therefore, this study proposes an indoor scene coloring design model that combines improved K-means clustering and simulated annealing algorithm for this important task. Based on the analysis of indoor scene coloring, particle swarm optimization algorithm is used to optimize K-means clustering to achieve color classification. Combined with simulated annealing algorithm, adaptive adjustment of lighting conditions is achieved to enhance the naturalness and realism of coloring. These results confirmed that the proposed method had the highest average F-value, with an average F-value of 92.524 and 143.601 on both datasets, respectively. The average ARI values were 0.361 and 0.897, respectively. The designed algorithm performed the best and converged faster than other three. Therefore, the proposed method can effectively ensure the consistency between the distribution of data objects after clustering and the actual situation. For indoor scene coloring design, it has important practical significance and provides new possible paths for improving design efficiency and prediction accuracy.

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Section: Related Workmentioning

confidence: 54%

Interior Scene Coloring Design Model Combining Improved K-means and SAA

2024

SCPE

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“…Early stage shrinkage cracks exacerbate these issues [3] and can lead to pavement collapse [4]. The poor tensile strength of concrete promotes faulting, with curling as a problem [5], which is compounded by excessive curling and warping from automotive stresses [6]. Thermal stresses cause warping and curling, which leads to pavement faults [7].…”

Section: Introductionmentioning

confidence: 99%

A Study on Mechanical Properties of Environmentally friendly Concrete Incorporating Banana Fiber and Banana Leaf Ash

Shah,

Ali

2024

The 1st International Online Conference on Buildings

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“…As a result, several prediction models, considering the combination of RS and ML algorithms, have been effectively used in recent decades to estimate and forecast water quality characteristics within bodies. These algorithms include artificial neural networks 17 – 21 , SVR 22 – 26 , random forest 27 – 35 , decision tree 36 – 41 , logistic regression, Naïve Bayes 42 , 43 , KNN 44 – 47 , and boosting algorithms 48 , 49 .…”

Section: Introductionmentioning

confidence: 99%

Mapping reservoir water quality from Sentinel-2 satellite data based on a new approach of weighted averaging: Application of Bayesian maximum entropy

Nikoo,

Zamani,

Zadeh

et al. 2024

Sci Rep

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In regions like Oman, which are characterized by aridity, enhancing the water quality discharged from reservoirs poses considerable challenges. This predicament is notably pronounced at Wadi Dayqah Dam (WDD), where meeting the demand for ample, superior water downstream proves to be a formidable task. Thus, accurately estimating and mapping water quality indicators (WQIs) is paramount for sustainable planning of inland in the study area. Since traditional procedures to collect water quality data are time-consuming, labor-intensive, and costly, water resources management has shifted from gathering field measurement data to utilizing remote sensing (RS) data. WDD has been threatened by various driving forces in recent years, such as contamination from different sources, sedimentation, nutrient runoff, salinity intrusion, temperature fluctuations, and microbial contamination. Therefore, this study aimed to retrieve and map WQIs, namely dissolved oxygen (DO) and chlorophyll-a (Chl-a) of the Wadi Dayqah Dam (WDD) reservoir from Sentinel-2 (S2) satellite data using a new procedure of weighted averaging, namely Bayesian Maximum Entropy-based Fusion (BMEF). To do so, the outputs of four Machine Learning (ML) algorithms, namely Multilayer Regression (MLR), Random Forest Regression (RFR), Support Vector Regression (SVRs), and XGBoost, were combined using this approach together, considering uncertainty. Water samples from 254 systematic plots were obtained for temperature (T), electrical conductivity (EC), chlorophyll-a (Chl-a), pH, oxidation–reduction potential (ORP), and dissolved oxygen (DO) in WDD. The findings indicated that, throughout both the training and testing phases, the BMEF model outperformed individual machine learning models. Considering Chl-a, as WQI, and R-squared, as evaluation indices, BMEF outperformed MLR, SVR, RFR, and XGBoost by 6%, 9%, 2%, and 7%, respectively. Furthermore, the results were significantly enhanced when the best combination of various spectral bands was considered to estimate specific WQIs instead of using all S2 bands as input variables of the ML algorithms.

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Optimized prediction models for faulting failure of Jointed Plain concrete pavement using the metaheuristic optimization algorithms

Cited by 15 publications

References 23 publications

Interior Scene Coloring Design Model Combining Improved K-means and SAA

Interior Scene Coloring Design Model Combining Improved K-means and SAA

A Study on Mechanical Properties of Environmentally friendly Concrete Incorporating Banana Fiber and Banana Leaf Ash

Mapping reservoir water quality from Sentinel-2 satellite data based on a new approach of weighted averaging: Application of Bayesian maximum entropy

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