Analytical fragility relation for buried cast iron pipelines with lead-caulked joints based on machine learning algorithms

Zhao, Ning; Li, Dian-Qing; Gu, Shi-Xiang; Du, Wenqi

doi:10.1177/87552930231209195

Cited by 27 publications

(3 citation statements)

References 38 publications

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“…Various machine learning (ML) algorithms approaches have been widely considered in the recent past that are capable of accurately predicting many real-world problems [26][27][28][29] . The recently developed AI techniques include artificial neural networks (ANNs) 30 , genetic-based programming 31 , eXtreme gradient boosting (XGBoost) 32,33 , multivariate adaptive regression splines (MARS) 34 , alternate decision trees (ADTs), logistic regression (LR), M5 model trees, genetic algorithm (GA) among others 35,36 .…”

mentioning

confidence: 99%

ANN-based swarm intelligence for predicting expansive soil swell pressure and compression strength

Jalal,

Iqbal,

Khan

et al. 2024

Sci Rep

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This research suggests a robust integration of artificial neural networks (ANN) for predicting swell pressure and the unconfined compression strength of expansive soils (PsUCS-ES). Four novel ANN-based models, namely ANN-PSO (i.e., particle swarm optimization), ANN-GWO (i.e., grey wolf optimization), ANN-SMA (i.e., slime mould algorithm) alongside ANN-MPA (i.e., marine predators’ algorithm) were deployed to assess the PsUCS-ES. The models were trained using the nine most influential parameters affecting PsUCS-ES, collected from a broader range of 145 published papers. The observed results were compared with the predictions made by the ANN-based metaheuristics models. The efficacy of all these formulated models was evaluated by utilizing mean absolute error (MAE), Nash–Sutcliffe (NS) efficiency, performance index ρ, regression coefficient (R2), root mean square error (RMSE), ratio of RMSE to standard deviation of actual observations (RSR), variance account for (VAF), Willmott’s index of agreement (WI), and weighted mean absolute percentage error (WMAPE). All the developed models for Ps-ES had an R significantly > 0.8 for the overall dataset. However, ANN-MPA excelled in yielding high R values for training dataset (TrD), testing dataset (TsD), and validation dataset (VdD). This model also exhibited the lowest MAE of 5.63%, 5.68%, and 5.48% for TrD, TsD, and VdD, respectively. The results of the UCS model’s performance revealed that R exceeded 0.9 in the TrD. However, R decreased for TsD and VdD. Also, the ANN-MPA model yielded higher R values (0.89, 0.93, and 0.94) and comparatively low MAE values (5.11%, 5.67, and 3.61%) in the case of PSO, GWO, and SMA, respectively. The UCS models witnessed an overfitting problem because the aforementioned R values of the metaheuristics were 0.62, 0.56, and 0.58 (TsD), respectively. On the contrary, no significant observation was recorded in the VdD of UCS models. All the ANN-base models were also tested using the a-20 index. For all the formulated models, maximum points were recorded to lie within ± 20% error. The results of sensitivity as well as monotonicity analyses depicted trending results that corroborate the existing literature. Therefore, it can be inferred that the recently built swarm-based ANN models, particularly ANN-MPA, can solve the complexities of tuning the hyperparameters of the ANN-predicted PsUCS-ES that can be replicated in practical scenarios of geoenvironmental engineering.

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mentioning

confidence: 99%

ANN-based swarm intelligence for predicting expansive soil swell pressure and compression strength

Jalal,

Iqbal,

Khan

et al. 2024

Sci Rep

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“…It is practically necessary to assess and monitor geotechnical structure deformations under seismic loadings, which attract great attentions from researchers (Mi et al, 2023;Zhao et al, 2024). The Nested Newmark method, based on the classical Newmark single sliding method, originally proposed by Leshchinsky (Du et al, 2023), considers a two-dimensional translational failure model of a simple homogeneous slope at the ultimate limit state under a seismic excitation.…”

Section: Proposed 3d Nested Newmark Methodsmentioning

confidence: 99%

Seismically-induced permanent displacements of slopes using 3D Nested Newmark method

Li,

Tong,

Wang

et al. 2024

Front. Earth Sci.

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The Newmark method is a classic method for evaluating the permanent displacements of a slope under seismic loads. This study aims at proposing a three-dimensional nested Newmark method (3D-NNM) in the framework of the kinematic theorem of limit analysis. The classical three-dimensional rotational failure mechanism is discretized into a series of nested rotating wedges, each of which is subjected to a corresponding yield acceleration determined by employing the work rate balance, and each of which produces relative displacements under seismic excitations when it exceeds the yield acceleration. The total permanent displacement profile is further obtained by integration of the relative displacements from the slope toe to the slope crest. The obtained results show that the proposed 3D-NNM can effectively evaluate the permanent displacement profile of slopes under earthquakes, and the proposed 3D-NNM improves the Leshchinsky’s 2D nested Newmark method by 30.7%; the obtained total horizontal displacement at the slope middle height reduces with the number of nested blocks, but increases with the increasing of the slope-width-to-height ratios. Besides, the traditional Newmark method with a single sliding block tends to overestimate the permanent displacements of slope under seismic shakings.

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“…Therefore, most of the research on mobile terminal location information focus on studying and predicting individual mobility patterns. Such analysis of individual mobility patterns, combined with the information from users' social network applications, can be suitably applied to public affairs management, such as user profiling, service recommendations, and market predictions [23]. Communication data providers can also upload smartphone location data to cloud servers for real-time analysis and storage.…”

Section: Principles Of Smartphone Location Big Datamentioning

confidence: 99%

Rapid determination of seismic influence field based on mobile communication big data—A case study of the Luding Ms 6.8 earthquake in Sichuan, China

Li,

Tan,

Tong

et al. 2024

PLoS ONE

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Smartphone location data provide the most direct field disaster distribution data with low cost and high coverage. The large-scale continuous sampling of mobile device location data provides a new way to estimate the distribution of disasters with high temporal–spatial resolution. On September 5, 2022, a magnitude 6.8 earthquake struck Luding County, Sichuan Province, China. We quantitatively analyzed the Ms 6.8 earthquake from both temporal and geographic dimensions by combining 1,806,100 smartphone location records and 4,856 spatial grid locations collected through communication big data with the smartphone data under 24-hour continuous positioning. In this study, the deviation of multidimensional mobile terminal location data is estimated, and a methodology to estimate the distribution of out-of-service communication base stations in the disaster area by excluding micro error data users is explored. Finally, the mathematical relationship between the seismic intensity and the corresponding out-of-service rate of communication base stations is established, which provides a new technical concept and means for the rapid assessment of post-earthquake disaster distribution.

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Analytical fragility relation for buried cast iron pipelines with lead-caulked joints based on machine learning algorithms

Cited by 27 publications

References 38 publications

ANN-based swarm intelligence for predicting expansive soil swell pressure and compression strength

ANN-based swarm intelligence for predicting expansive soil swell pressure and compression strength

Seismically-induced permanent displacements of slopes using 3D Nested Newmark method

Rapid determination of seismic influence field based on mobile communication big data—A case study of the Luding Ms 6.8 earthquake in Sichuan, China

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