Sustainable Development and Performance Assessment of Clay-Based Geopolymer Bricks Incorporating Fly Ash and Sugarcane Bagasse Ash DOI
Noor Yaseen, Muhammad Irfan‐ul‐Hassan,

Abaid-ur-Rehman Saeed

et al.

Journal of Materials in Civil Engineering, Journal Year: 2022, Volume and Issue: 34(4)

Published: Jan. 28, 2022

Emission of carbon dioxide (CO2) either from the firing clay bricks or cement production, contributes considerably toward global warming. Conversely, production is inevitable since a large number are needed to fulfill housing sector demand. In this study, silty clay-based geopolymer were produced incorporating fly ash and sugarcane bagasse ash. This was accomplished in two stages: laboratory phase that comprised cylindrical specimens, industrial whereby full-size based on results obtained phase. The developed with lesser energy input, i.e., forming pressure 7 MPa curing at ambient temperature. whole set mechanical durability properties newly brick yielded satisfactory conforming standard codes. Scanning electron microscopy (SEM) X-ray diffraction (XRD) revealed coexistence sodium aluminosilicate gel (N─ A─ S─ H) calcium hydrate (C─ H), which led dense microstructure resulting increased strength ensuring enhanced structure. environmental impact assessment confirmed ecofriendly utilization combination bricks. can have broad range applications, including wall panel making, jet grouting, deep mixing, mortar for masonry constructions, canal lining, grouting material used backfill during shield tunneling.

Language: Английский

Improved prediction of slope stability using a hybrid stacking ensemble method based on finite element analysis and field data DOI Creative Commons
Navid Kardani, Annan Zhou, Majidreza Nazem

et al.

Journal of Rock Mechanics and Geotechnical Engineering, Journal Year: 2020, Volume and Issue: 13(1), P. 188 - 201

Published: Nov. 23, 2020

Slope failures lead to catastrophic consequences in numerous countries and thus the stability assessment for slopes is of high interest geotechnical geological engineering researches. A hybrid stacking ensemble approach proposed this study enhancing prediction slope stability. In approach, we used an artificial bee colony (ABC) algorithm find out best combination base classifiers (level 0) determined a suitable meta-classifier 1) from pool 11 individual optimized machine learning (OML) algorithms. Finite element analysis (FEA) was conducted order form synthetic database training stage (150 cases) model while 107 real field cases were testing stage. The results by then compared with that obtained OML methods using confusion matrix, F1-score, area under curve, i.e. AUC-score. comparisons showed significant improvement ability has been achieved (AUC = 90.4%), which 7% higher than 82.9%). Then, further comparison undertaken between method basic classifier on prediction. prominent performance over method. Finally, importance variables studied linear vector quantization (LVQ)

Language: Английский

Citations

202
Ensemble learning framework for landslide susceptibility mapping: Different basic classifier and ensemble strategy DOI Creative Commons
Taorui Zeng, Liyang Wu, Dario Peduto

et al.

Geoscience Frontiers, Journal Year: 2023, Volume and Issue: 14(6), P. 101645 - 101645

Published: June 7, 2023

The application of ensemble learning models has been continuously improved in recent landslide susceptibility research, but most studies have no unified framework. Moreover, few papers discussed the applicability model mapping at township level. This study aims defining a robust framework that can become benchmark method for future research dealing with comparison different models. For this purpose, present work focuses on three basic classifiers: decision tree (DT), support vector machine (SVM), and multi-layer perceptron neural network (MLPNN) two homogeneous such as random forest (RF) extreme gradient boosting (XGBoost). hierarchical construction deep relied leading technologies (i.e., homogeneous/heterogeneous bagging, boosting, stacking strategy) to provide more accurate effective spatial probability occurrence. selected area is Dazhou town, located Jurassic red-strata Three Gorges Reservoir Area China, which strategic economic currently characterized by widespread risk. Based long-term field investigation, inventory counting thirty-three slow-moving polygons was drawn. results show do not necessarily perform better; instance, Bagging based DT-SVM-MLPNN-XGBoost performed worse than single XGBoost model. Amongst eleven tested models, Stacking RF-XGBoost model, ensemble, showed highest capability predicting landslide-affected areas. Besides, factor behaviors DT, SVM, MLPNN, RF reflected characteristics landslides reservoir area, wherein unfavorable lithological conditions intense human engineering activities water level fluctuation, residential construction, farmland development) are proven be key triggers. presented approach could used occurrence prediction similar regions other fields.

Language: Английский

Citations

76
Prevention/mitigation of natural disasters in urban areas DOI Creative Commons

Jinchun Chai,

Haoze Wu

Smart Construction and Sustainable Cities, Journal Year: 2023, Volume and Issue: 1(1)

Published: Aug. 9, 2023

Abstract Preventing/mitigating natural disasters in urban areas can indirectly be part of the 17 sustainable economic and social development intentions according to United Nations 2015. Four types disasters—flooding, heavy rain-induced slope failures/landslides; earthquakes causing structure failure/collapse, land subsidence—are briefly considered this article. With increased frequency climate change-induced extreme weathers, numbers flooding failures/landslides has recent years. There are both engineering methods prevent their occurrence, more effectively early prediction warning systems mitigate resulting damage. However, still cannot predicted an extent that is sufficient avoid damage, developing adopting structures resilient against earthquakes, is, featuring earthquake resistance, vibration damping, seismic isolation, essential tasks for city development. Land subsidence results from human activity, mainly due excessive pumping groundwater, which a “natural” disaster caused by activity. Countermeasures include effective regional and/or national freshwater management local water recycling groundwater. Finally, perspectives risk hazard prevention through enhanced field monitoring, assessment with multi-criteria decision-making (MCDM), artificial intelligence (AI) technology.

Language: Английский

Citations

45
Use of secondary additives in fly ash based soil stabilization for soft subgrades DOI

Hadi Karami,

J. Pooni, Dilan Robert

et al.

Transportation Geotechnics, Journal Year: 2021, Volume and Issue: 29, P. 100585 - 100585

Published: May 19, 2021

Language: Английский

Citations

80
Modelling of municipal solid waste gasification using an optimised ensemble soft computing model DOI
Navid Kardani, Annan Zhou, Majidreza Nazem

et al.

Fuel, Journal Year: 2020, Volume and Issue: 289, P. 119903 - 119903

Published: Dec. 19, 2020

Language: Английский

Citations

79
Tunnel boring machine vibration-based deep learning for the ground identification of working faces DOI Creative Commons
Mengbo Liu, Shaoming Liao, Yifeng Yang

et al.

Journal of Rock Mechanics and Geotechnical Engineering, Journal Year: 2021, Volume and Issue: 13(6), P. 1340 - 1357

Published: Oct. 22, 2021

Tunnel boring machine (TBM) vibration induced by cutting complex ground contains essential information that can help engineers evaluate the interaction between a cutterhead and itself. In this study, deep recurrent neural networks (RNNs) convolutional (CNNs) were used for vibration-based working face identification. First, field monitoring was conducted to obtain TBM data when tunneling in changing geological conditions, including mixed-face, homogeneous, transmission ground. Next, RNNs CNNs utilized develop prediction models, which then validated using testing dataset. The accuracy of long short-term memory (LSTM) bidirectional LSTM (Bi-LSTM) models approximately 70% with raw data; however, instantaneous frequency transmission, increased 80%. Two types CNNs, GoogLeNet ResNet, trained tested time-frequency scalar diagrams from continuous wavelet transformation. CNN an greater than 96%, performed significantly better RNN models. ResNet-18, 98.28%, best. When sample length set as rotation period, achieved highest while proposed model simultaneously high feedback efficiency. could promptly identify conditions at without stopping normal process, parameters be adjusted optimized timely manner based on predicted results.

Language: Английский

Citations

76
Distribution characteristics and utilization of shallow geothermal energy in China DOI
Ye‐Shuang Xu, Xuwei Wang, Shui‐Long Shen

et al.

Energy and Buildings, Journal Year: 2020, Volume and Issue: 229, P. 110479 - 110479

Published: Sept. 15, 2020

Language: Английский

Citations

71
Dynamic prediction of mechanized shield tunneling performance DOI
Ruohan Wang, Dianqing Li, Elton J. Chen

et al.

Automation in Construction, Journal Year: 2021, Volume and Issue: 132, P. 103958 - 103958

Published: Sept. 15, 2021

Language: Английский

Citations

69
Real-time prediction of shield moving trajectory during tunnelling using GRU deep neural network DOI
Nan Zhang, Ning Zhang, Qian Zheng

et al.

Acta Geotechnica, Journal Year: 2021, Volume and Issue: 17(4), P. 1167 - 1182

Published: July 30, 2021

Language: Английский

Citations

68
Machine Learning-Based Modelling of Soil Properties for Geotechnical Design: Review, Tool Development and Comparison DOI
Pin Zhang, Zhen‐Yu Yin,

Yin-Fu Jin

et al.

Archives of Computational Methods in Engineering, Journal Year: 2021, Volume and Issue: 29(2), P. 1229 - 1245

Published: July 5, 2021

Language: Английский

Citations

64