Application of artificial intelligence in predicting ground settlement on earth slope

Mamat, Rufaizal Che; Kasa, Anuar; Razali, Siti Fatin Mohd; Samad, Abd Manan; Ramli, Azuin; Yazid, Muhamad Razuhanafi Mat

doi:10.1063/1.5121094

Cited by 8 publications

(5 citation statements)

References 29 publications

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“…This allows it to effectively map complex, high-dimensional data sets onto a lower-dimensional space, providing a powerful and flexible tool for a range of applications. Ultimately, the BP neural network is a powerful tool that can help unlock the potential of big data [22].…”

Section: B Calculation Of Related Parametersmentioning

confidence: 99%

Research on Settlement Prediction of Building Foundation in Smart City Based on BP Network

Wei¹

2023

IJACSA

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In the construction process of high-rise buildings, it is necessary to predict the settlement and deformation of the foundation, and the current prediction methods are mainly based on empirical theoretical calculations and methods and more accurate numerical analysis methods. In the face of the interference of complex and ever-changing terrain and parameter values on prediction methods, in order to accurately determine the settlement of building foundations, this study designed a smart city building foundation settlement prediction method based on BP neural network. Firstly, a real-time dynamic monitoring unit for building foundation settlement was constructed using Wireless Sensor Network (WSN) technology. Then, the monitoring data was used to calculate the relevant parameters of building foundation settlement through layer sum method. Finally, input the monitoring data into the BP network results, adjust the weights of the output layer and hidden layer using settlement related parameters, and output the settlement prediction results of the smart city building foundation through training. The study selected average error and prediction time as evaluation criteria to test the feasibility of the method proposed in this article. This method can effectively predict foundation settlement, with an average prediction error always less than 4% and a prediction process time always less than 49ms.

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Section: B Calculation Of Related Parametersmentioning

confidence: 99%

Research on Settlement Prediction of Building Foundation in Smart City Based on BP Network

Wei¹

2023

IJACSA

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“…This method does not require soil data parameters but still produces reliable results. The Asaoka method has been proven reliable for estimating field settlement [9] and can be applied with or without a vertical drain [10]. The steps for predicting consolidation settlement using the Asaoka method are: 1.…”

Section: Fig 2 Stratification Of Soil At Sta 23+650 Of the Semarang-d...mentioning

confidence: 99%

Consolidation settlement prediction and monitoring of toll road embankment at STA 23+650 Semarang–Demak Toll Road section

Sari,

Wardani,

Muntohar

et al. 2023

E3S Web Conf.

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The development of toll road projects in Indonesia is experiencing rapid growth. However, due to the large number of toll roads being constructed over soft soil areas, it is crucial to address the issue of consolidation settlement. This is because settlement can lead to differential or excessive settlement, which potentially damages the structures built on such soil. Unfortunately, consolidation settlement on soft soils is a time-consuming process. Thus, a consolidation acceleration method is needed, one of which is to use a combination of PVD–Preloading. This method can also increase the soil’s bearing capacity, thereby contributing to the preservation and protection of systems that support human activities while maintaining a balance with nature. Therefore, this study aims to investigate consolidation settlement employing PVD–Preloading methods. The analysis focused on a case study conducted at STA 23+650 of the Semarang-Demak Toll Road section. The analysis employed the Asaoka method, integrating field monitoring instruments, along with the Terzaghi method for one-dimensional consolidation. The results showed that the settlement results from the Asaoka and Terzaghi methods correlate with the settlement plate instrumentation in the field.

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“…Once such devices and computers have to gain information, by making appropriate assumptions on current issues at hand, they should be able to think. AI approach massively to solve the complex work, save the budget and improve the service of quality [6]. This technology is one of the branches of science in making objects intelligences as human being [7].…”

Section: Literature Reviewmentioning

confidence: 99%

Artificial intelligence application for predicting slope stability on soft ground: a comparative study

Omar¹,

Mamat²,

Rasam³

et al. 2021

IJATEE

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This paper aimed to estimate the slope stability on soft ground by utilising Artificial Intelligence (AI) method that is widely used in the past decade for prediction purposes. The slope stability is predicted using Factor of Safety (FoS) that is generated with Limit Equilibrium Method (LEM) such as Ordinary, Bishop, Janbu and Morgenstern-Price and the total of 233 random datasets in this study. The output of the FoS is also estimated using the input parameters of height of slope (H), unit weight slope aterial (γ), angle of slope (θ), Coefficient of cohesion (c), and internal angle friction (ϕ) that are proceeded with Artificial Neural Networks (ANN) and Adaptive Neural Fuzzy-Logic Inference Systems (ANFIS). The final selected model of the slope stability is tested with the real data to produce a better result of the potential prediction in the accepted range of accuracy.

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Application of artificial intelligence in predicting ground settlement on earth slope

Cited by 8 publications

References 29 publications

Research on Settlement Prediction of Building Foundation in Smart City Based on BP Network

Research on Settlement Prediction of Building Foundation in Smart City Based on BP Network

Consolidation settlement prediction and monitoring of toll road embankment at STA 23+650 Semarang–Demak Toll Road section

Artificial intelligence application for predicting slope stability on soft ground: a comparative study

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