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Development of AI-based Prediction and Assessment Program for Tunnelling Impact
유충식,SYED AIZAZ HAIDER2,양재원,TABISH ALI 한국지반신소재학회 2019 한국지반신소재학회 논문집 Vol.18 No.4
In this paper the development and implementation of an artificial intelligence (AI)-based Tunnelling Impact prediction and assessment program (SKKU-iTunnel) is presented. Program predicts tunnelling induced surface settlement and groundwater drawdown by utilizing well trained ANNs and uses these predicted values to perform the damage assessment likely to occur in nearby structures and pipelines/utilities for a given tunnel problem. Generalised artificial neural networks (ANNs) were trained, to predict the induced parameters, through databases generated by combining real field data and numerical analysis for cases that represented real field conditions. It is shown that program equipped with carefully trained ANN can predict tunnel impact assessments and perform damage assessments quiet efficiently and comparable accuracy to that of numerical analysis. This paper describes the idea and implementation details of the SKKU-iTunnel with an example for demonstration.
Artificial Intelligence (AI)-based Deep Excavation Designed Program
유충식,HAIDER SYED AIZAZ,Qaisar Abbas,양재원 한국지반신소재학회 2018 한국지반신소재학회 논문집 Vol.17 No.4
This paper presents the development and implementation of an artificial intelligence (AI)-based deep excavation induced wall and ground displacements and wall support member forces prediction program (ANN-EXCAV). The program has been developed in a C# environment by using the well-known AI technique artificial neural network (ANN). Program used ANN to predict the induced displacement, groundwater drawdown and wall and support member forces parameters for deep excavation project and run the stability check by comparing predict values to the calculated allowable values. Generalised ANNs were trained to predict the said parameters through databases generated by numerical analysis for cases that represented real field conditions. A practical example to run the ANN-EXCAV is illustrated in this paper. Results indicate that the program efficiently performed the calculations with a considerable accuracy, so it can be handy and robust tool for preliminary design of wall and support members for deep excavation project.
Artificial Intelligence (AI)-based Deep Excavation Designed Program
Yoo, Chungsik,Aizaz, Haider Syed,Abbas, Qaisar,Yang, Jaewon Korean Geosynthetics Society 2018 한국지반신소재학회 논문집 Vol.17 No.4
This paper presents the development and implementation of an artificial intelligence (AI)-based deep excavation induced wall and ground displacements and wall support member forces prediction program (ANN-EXCAV). The program has been developed in a C# environment by using the well-known AI technique artificial neural network (ANN). Program used ANN to predict the induced displacement, groundwater drawdown and wall and support member forces parameters for deep excavation project and run the stability check by comparing predict values to the calculated allowable values. Generalised ANNs were trained to predict the said parameters through databases generated by numerical analysis for cases that represented real field conditions. A practical example to run the ANN-EXCAV is illustrated in this paper. Results indicate that the program efficiently performed the calculations with a considerable accuracy, so it can be handy and robust tool for preliminary design of wall and support members for deep excavation project.
Development of AI-based Prediction and Assessment Program for Tunnelling Impact
Yoo, Chungsik,HAIDER, SYED AIZAZ,Yang, Jaewon,ALI, TABISH Korean Geosynthetics Society 2019 한국지반신소재학회 논문집 Vol.18 No.4
In this paper the development and implementation of an artificial intelligence (AI)-based Tunnelling Impact prediction and assessment program (SKKU-iTunnel) is presented. Program predicts tunnelling induced surface settlement and groundwater drawdown by utilizing well trained ANNs and uses these predicted values to perform the damage assessment likely to occur in nearby structures and pipelines/utilities for a given tunnel problem. Generalised artificial neural networks (ANNs) were trained, to predict the induced parameters, through databases generated by combining real field data and numerical analysis for cases that represented real field conditions. It is shown that program equipped with carefully trained ANN can predict tunnel impact assessments and perform damage assessments quiet efficiently and comparable accuracy to that of numerical analysis. This paper describes the idea and implementation details of the SKKU-iTunnel with an example for demonstration.
유충식,양재원,Qaisar Abbas,HAIDER SYED AIZAZ 한국지반신소재학회 2018 한국지반신소재학회 논문집 Vol.17 No.4
This research concerns the prediction method for ground movement and wall member force due to determination structural stability check and failure check during deep excavation construction. First, research related with excavation influence parameters is conducted. Then, numerical analysis for various excavation conditions were conducted using Finite Element Method and Beam-column elasto-plasticity method. Excavation analysis database was then constructed. Using this database, development of ANN (artificial neural network) was performed for each ground movements and using structural member forces. By comparing the numerical analysis results with ANN’s prediction, it is validated that development of ANN can be used efficient for prediction of ground movement and structural member forces in deep excavation site. 본 연구에서는 깊은 굴착에 따른 인접구조물의 손상 평가 및 벽체 구조물의 안정성 평가를 하기 위한 지표의 거동 및 벽체부재력의 효율적인 예측기법에 대한 내용을 다루었다. 우선적으로 지표의 거동 및 벽체 부재력에 영향을 미치는 매개 변수에대한 연구를 수행하였고, 이를 토대로 다양한 굴착 조건에 대해 수치해석을 실시한 결과를 통해 데이터베이스를 구축하였다. 구축된 데이터베이스를 토대로 벽체의 부재력과 지표의 거동 각각의 해석 결과에 대한 인공신경망 엔진 학습을 수행하였으며학습된 인공신경망을 이용하여 예측된 결과와 사용된 데이터베이스의 결과를 비교하여 인공신경망 엔진이 벽체의 부재력및 지표의 거동예측에 효율적임을 검증하였다.