Risk Assessment of Debris Flow in Ya’an City Based on BP Neural Network

Liu, Xiaoyu; Shu, Xiaolong; Liu, Xing; Duan, Zhe; Ran, Zhiyi

doi:10.1088/1757-899x/794/1/012006

Cited by 8 publications

(2 citation statements)

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“…It is the transition zone from the Tibet Plateau to the basin. The terrain is high in the north, west and south, while low in the middle and east [25]. The climate type is subtropical monsoon humid; the annual average temperature is more than 14 • C, the abundant rainfall here makes it so the annual rainy days are more than 200 days and the average annual precipitation is about 1800 mm.…”

Section: Site Descriptionmentioning

confidence: 99%

The Effect of Soil Surface Mounds and Depressions on Runoff

Liang

Feng

et al. 2022

Sustainability

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Surface mounds and depressions are the basic patterns of microtopography. Their geometric forms and physical properties affect rainfall infiltration, runoff generation and runoff confluence process. In this study, soil beds were set up with seven different types of microtopography to study the effects of surface mounds and depressions on runoff. They were the control check (CK), alternate mounds (AM), continuous mounds (CM), alternate depressions (AD), continuous depressions (CD), alternate mounds and depressions (AMD) and continuous mounds and depressions (CMD). There was only one microtopography type for monomorphic surface relief (MSR) while two for compound surface relief (CSR). All soil beds were exposed under 60, 90 or 120 mm/h rainfall intensity for 90 min. The main results are as follows: surface mounds could promote surface runoff, triggering and shortening runoff generation time, while surface depressions showed contrary results. Whether there was an interval between mounds or depressions also affected the characteristics of runoff. The runoff generation time was 3.8–5.0 times higher for continuous slope than for interval slope, while the runoff yield and runoff coefficient both decreased by approximately 40%. CSR can significantly neutralize the flow-promoting effects of the mounds and the flow-inhibiting effects of the depressions, making the runoff yield and runoff process present a neutral state between the mounds and depressions. CSR prolongs runoff generation time from 1–10 min of MSR to 5–16 min. The runoff yield of CSR presented as 0.12, between 0.17 for mounds and 0.10 for depressions, and so did the runoff coefficient and hydrodynamic parameters. In addition, with rainfall intensity increased, the runoff pattern of CSR and MSR became more similar to each other, and the retarding effects of topography on overland flow were more effective.

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Section: Site Descriptionmentioning

confidence: 99%

The Effect of Soil Surface Mounds and Depressions on Runoff

Liang

Feng

et al. 2022

Sustainability

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“…Abdollahzadeh and Rastgoo [8] adopted fault tree and event tree analysis methods based on fuzzy logic to evaluate the risks in bridge construction projects. Liu et al [9] studied the construction risk of the double-walled coffer dam pier and applied the fuzzy fault tree theory to evaluate its reliability and safety. Wang and Chen [10] proposed a system decision support method for uncertainty safety risk analysis of subway construction projects based on the fuzzy comprehensive Bayesian network, indicating that this method is effective in estimating the risk level of subway construction projects under uncertain conditions.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis and Evaluation of Bridge Construction Risk with Multiple Intelligent Algorithms

2022

Mathematical Problems in Engineering

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Bridge collapse in the construction process is one of the most common bridge accidents; we can see the importance of construction to ensure bridge safety. Site construction safety management mainly relies on checklist evaluation, but the evaluation results are often affected by the ability and experience of the evaluator. Using an artificial intelligence algorithm to realize rapid and accurate risk assessment of the bridge construction process is an effective way and development direction to solve the above problems. In view of the uncertain factors in the process of bridge construction, this paper analyzes and classifies the man-made and environmental risk factors that affect the bridge construction stage by using the 4M1E analysis method. Typical artificial intelligence algorithms are used to construct risk assessment models for bridge construction. And the random forest algorithm is used to analyze and verify the construction example of the FIU bridge.

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