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	J. of Soft Computing in Civil Engineering (Followers: 1)

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Journal of Soft Computing in Civil Engineering
Number of Followers: 1

This is an Open Access Journal

Open Access journal
ISSN (Online) 2588-2872
Published by Pouyan Press

[1 journal]

Environmental Risk Management of Eyvashan Dam Using Traditional-FMEA and
FIS-FMEA Methods
- Abstract: The implementation of large dam construction projects, despite the positive economic and social effects on the region, may endanger the development of the region with long-term negative effects. Therefore, it seems necessary to pay attention to this issue to reduce the negative effects of large dam construction projects and to consider them in the evaluation of benefits and costs for policy and codified planning in the water resources sector. In this research, Shannon's entropy-TOPSIS methodology and fuzzy TOPSIS methods have been used to identify and prioritize the environmental risk of Eyvashan dam in the construction and operation phases. Also, in this article, to improve the risk management of earthen dams, a comprehensive review was presented to overcome the disadvantages of traditional FMEA through the improvement of FMEA, with the combination of Fuzzy Inference System (FIS). The results show that in both Shannon's entropy-TOPSIS and fuzzy TOPSIS methods, soil erosion in the construction phase and aquatic in the exploitation phase is the major environmental risks. Evaluation of Risk Priority Number (RPN) in both traditional RPN and FIS-RPN modes shows a significant increase in RPN in fuzzy mode compared to the traditional method in all risk environments. Therefore, the urgency of action evaluation criteria in the FIS-FMEA mode is much more serious than in the traditional FMEA mode and requires more accurate identification and monitoring of risk environments.
Computer Vision-Based Recognition of Pavement Crack Patterns Using Light
Gradient Boosting Machine, Deep Neural Network, and Convolutional Neural
Network
- Abstract: The performance and serviceability of asphalt pavements have a direct influence on people's daily lives. Timely detection of pavement cracks is crucial in the task of periodic pavement survey. This paper proposes and verifies a novel computer vision-based method for recognizing pavement crack patterns. Image processing techniques, including Gaussian steerable filters, projection integrals, and image texture analyses, are employed to characterize the surface condition of asphalt pavement roads. Light Gradient Boosting Machine, Deep Neural Network, and Convolutional Neural Network are employed to recognize various patterns including longitudinal, transverse, diagonal, minor fatigue, and severe fatigue cracks. A dataset, including 12,000 samples, has been collected to construct and verify the computer vision-based approaches. Based on experiments, it can be found that all three machine learning models are capable of delivering good categorization results with an accuracy rate > 0.93 and Cohen's Kappa coefficient > 0.76. Notably, the Light Gradient Boosting Machine has achieved the most desired performance with an accuracy rate > 0.96 and Cohen's Kappa coefficient > 0.88.
Tree-Based Techniques for Predicting the Compression Index of Clayey Soils
- Abstract: Compression index is an effective assessment of primary consolidation settlement of clayey soils, but the process of obtaining compression index is time-consuming and laborious. Thus, in the present study, we developed two classical tree-based techniques: random forest (RF) and extreme gradient boosting (XGBoost), to predict the compression index of clayey soils. To establish these two models, we collected an available dataset—including 391 consolidation tests for soils—from previously published research. The dataset consists of six physical parameters, including the initial void ratio, natural water content, liquid limit, plastic index, specific gravity, and soil compression index. The first five parameters are the models’ inputs while the compression index is the models’ output. We trained both two tree-based models using 90% of the entire dataset and used the remaining 10% to assess the well-trained models, which is consistent with the published research. Several statistical metrics, such as coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), are the criteria for assessing the models’ performance. The results show that the RF model has better accuracy in predicting compression index compared with the XGBoost model because it outperforms the XGBoost model both on the training and testing datasets. The performance of the RF model is R2 of 0.928 and 0.818, RMSE of 0.016 and 0.025, MAPE of 7.046% and 10.082%, and MAE of 0.012 and 0.020 on the training and testing datasets, respectively. The sensitivity analysis reveals that the initial void ratio has a significant impact on the compression index of clayey soils.