하천 오염물질 혼합 해석을 위한 저장대 모형의 매개변수 산정법 및 경험식 개발

학위논문(석사)--서울대학교 대학원 :공과대학 건설환경공학부,2019. 8. 서일원.Analyses of solute transport and retention mechanism are essential to manage water quality and river ecosystem. As reported by tracer injection studies that have been conducted to identify solute transport mechanism, concentration curves measured in natural stream have steep rising and long tail parts. This phenomenon is due to solute exchange process between transient storage zones and the main river stream. The transient storage model (TSM) is one of the most widely used models for describing solute transport in natural stream, taking transient storage exchange process into consideration. In order to use this model, calibration of four TSM parameters is necessary. Inverse modelling using measured breakthrough curves (BTCs) from tracer injection test is general method for TSM parameter calibration. However, it is not feasible to carry out performing tracer injection tests, for every parameter calibration. For that reasons, empirical formulae with hydraulic data, which is comparatively easier to obtain, have been proposed for the purpose of parameter estimation. This study presents two methods for TSM parameter estimation. At first, inverse modelling method employing global optimization framework Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL), that incorporating famous evolutionary algorithms in water resource management field, was suggested. Second, TSM parameter empirical equations were derived adopting Multigene Genetic Programming (MGGP) based symbolic regression library GPTIPS and using Principal Components Regression (PCR). In terms of general performance, equations of this study were superior to published empirical equations.하천의 수질을 관리하기 위해서는 자연하천에서 유입된 물질이 이송되고 지체되는 메카니즘을 규명하고 이해하는 것이 필요하다. 하천에서의 물질 혼합을 이해하기 위해 수행된 추적자 실험 연구들에 따르면 자연하천에서 계측되는 농도곡선에서는 가파른 상승부와 긴 꼬리기 관측되는 것으로 알려졌다. 이러한 현상은 주로 물질이 흐르는 본류대와 잠시 물질이 포획되었다가 재방출되는 본류대와 저장대 간의 물질교환 효과 때문에 일어난다고 알려져 있다. 이러한 저장대 물질교환 효과를 모사하는 저장대모형 중 Transient Storage zone Model (TSM)은 가장 광범위하게 이용되는 모형으로, 이를 이용하기 위해선 네 가지의 저장대 매개변수를 보정하여야 한다. 네 가지 저장대 매개변수를 결정하는 방법으로는 일반적으로 현장실험에서 측정된 농도곡선을 이용한 역산모형이 이용된다. 그러나 매개변수가 필요할 때마다 추적자실험을 수행하여 역산모형을 이용하는 것은 현실적으로 불가능한 경우가 있어 이러한 경우에는 비교적 취득하기 쉬운 수리지형학적 인자들을 이용해 매개변수를 산정하는 방법이 이용될 수 있다. 따라서 본 연구에서는 TSM 매개변수를 결정하기 위해 두 가지 방법을 제시하였다. 첫 번째로, 전역 최적화 프레임워크인 Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL)을 이용한 역산모형 기반 TSM 매개변수 산정 프레임워크를 제시하였다. 둘째로는 기호회귀법 라이브러리인 GPTIPS를 이용한 다중유전자 유전 프로그래밍(Multigene Genetic Programming, MGGP) 과 주성분회귀법(Principal Components Regression, PCR)을 통해 네 가지 매개변수 별로 각 두 개씩의 경험식이 개발되었다. 개발된 경험식들의 성능평가 결과, 선행 연구에서 제시된 저장대 매개변수 식에 비해 본 연구에서 제시된 방법이 대체적으로 우수한 것으로 나타났다. 결과적으로 본 연구에서는 분석을 통해 실무적으로 활용 가능한 TSM 매개변수 산정 프레임워크와 경험식들이 제시되었으며, 이 방법들은 추적자 실험 자료의 유무에 따라 TSM의 매개변수 결정에 유용하게 사용될 것으로 기대된다.Chapter 1. Introduction 1 1.1 Necessity and Background of Research 1 1.2 Objectives 12 Chapter 2. Theoretical Background 15 2.1 Transient Storage Model 15 2.1.1. Mechanisms of Transient Storage 15 2.1.2. Models Accounting for Transient Storage 21 2.1.2.1 The one Zone Transient Storage Model (1Z-TSM) 24 2.1.2.2 The two Zone Transient Storage Model (2Z-TSM) 25 2.1.2.3 The Continuous Time Random Walk Approach (CTRW) 26 2.1.2.4 The Modified Advection Dispersion Model (MADE) 27 2.1.2.5 The Fractional Advection Dispersion Equation Model (FADE) 28 2.1.2.6 The Multirate Mass Transfer Model (MRMT) 29 2.1.2.7 The Advective Storage Path Model (ASP) 30 2.1.2.8 The Solute Transport in Rivers Model (STIR) 31 2.1.2.9 The Aggregate Dead Zone Model (ADZ) 34 2.2 Empirical Equations for Predicting Transient Storage Model Parameters 39 2.3 Parameter Estimation 47 2.3.1. The SC-SAHEL Framework 50 2.3.1.1 Modified Competitive Complex Evolution (MCCE) 52 2.3.1.2 Modified Frog Leaping (MFL) 52 2.3.1.3 Modified Grey Wolf Optimizer (GWO) 53 2.3.1.4 Modified Differential Evolution (DE) 53 2.4 Regression Method 54 2.4.1. The Multi-Gene Genetic Programming (MGGP) 56 2.4.1.1 The Simple Genetic Programming 56 2.4.1.2 Scaled Symbolic Regression via Multi-Gene Genetic Programming 57 2.4.2. Evolutionary Polynomial Regression (EPR) 61 2.4.2.1 Main Flow of EPR Procedure 62 Chapter 3. Model Development 66 3.1 Numerical Model 66 3.1.1. Model Validation 69 3.2 Merger of TSM-SC-SAHEL 73 3.3 Further assessments for the parameter estimation framework 76 3.3.1. Tracer Test Description 76 3.3.2. Grid Independency of Estimation 81 3.3.3. Choice of Optimization Setting 85 Chapter 4. Development of Formulae for Predicting TSM Parameter 91 4.1 Dimensional Analysis 91 4.2 Data Collection via Meta Analysis 95 4.3 Formulae Development 106 Chapter 5. Result and Discussion 110 5.1 Model Performances 110 5.2 Sensitivity Analysis 118 5.3 In-stream Application of Empirical Equations 130 Chapter 6. Conclusion 140 References 144 Appendix. I. The mean, minimum, and maximum values of the model fitness value and number of evolution using the SC-SAHEL with single-EA and multi-EA 159 Appendix. II. Used dimensionless datasets for development of empirical equations 161 국문초록 165Maste

A review of artificial intelligence applications in shallow foundations

Geotechnical engineering deals with materials (e.g. soil and rock) that, by their very nature, exhibit varied and uncertain behavior because of the imprecise physical processes associated with the formation of these materials. Modeling the behavior of such materials in geotechnical engineering applications is complex and sometimes beyond the ability of most traditional forms of physically based engineering methods. Artificial intelligence (AI) is becoming more popular and particularly amenable to modeling the complex behavior of most geotechnical engineering applications, including foundations, because it has demonstrated superior predictive ability compared to traditional methods. The main aim of this paper is to review the AI applications in shallow foundations and present the salient features associated with the AI modeling development. The paper also discusses the strengths and limitations of AI techniques compared to other modeling approaches

A Feasibility Study of BBP for predicting shear capacity of FRP reinforced concrete beams without stirrups.

yesShear failure of concrete elements reinforced with Fiber Reinforced Polymer (FRP) bars is generally brittle, requiring accurate predictions to avoid it. In the last decade, a variety of artificial intelligence based approaches have been successfully applied to predict the shear capacity of FRP Reinforced Concrete (FRP-RC). In this paper, a new approach, namely, biogeography-based programming (BBP) is introduced for predicting the shear capacity of FRP-RC beams based on test results available in the literature. The performance of the BBP model is compared with several shear design equations, two previously developed artificial intelligence models and experimental results. It was found that the proposed model provides the most accurate results in calculating the shear capacity of FRP-RC beams among the considered shear capacity models. The proposed BBP model can also correctly predict the trend of different influencing variables on the shear capacity of FRP-RC beams

OPTIMAL WATER QUALITY MANAGEMENT STRATEGIES FOR URBAN WATERSHEDS USING MACRO-LEVEL SIMULATION MODELS LINKED WITH EVOLUTIONARY ALGORITHMS

Urban watershed management poses a very challenging problem due to the varioussources of pollution and there is a need to develop optimal management models that canfacilitate the process of identifying optimal water quality management strategies. Ascreening level, comprehensive, and integrated computational methodology is developedfor the management of point and non-point sources of pollution in urban watersheds. Themethodology is based on linking macro-level water quality simulation models withefficient nonlinear constrained optimization methods for urban watershed management.The use of macro-level simulation models in lieu of the traditional and complexdeductive simulation models is investigated in the optimal management framework forurban watersheds. Two different types of macro-level simulation models are investigatedfor application to watershed pollution problems namely explicit inductive models andsimplified deductive models. Three different types of inductive modeling techniques areused to develop macro-level simulation models ranging from simple regression methodsto more complex and nonlinear methods such as artificial neural networks and geneticfunctions. A new genetic algorithm (GA) based technique of inductive modelconstruction called Fixed Functional Set Genetic Algorithm (FFSGA) is developed andused in the development of macro-level simulation models. A novel simplified deductivemodel approach is developed for modeling the response of dissolved oxygen in urbanstreams impaired by point and non-point sources of pollution. The utility of this inverseloading model in an optimal management framework for urban watersheds isinvestigated.In the context of the optimization methods, the research investigated the use of parallelmethods of optimization for use in the optimal management formulation. These includedan evolutionary computing method called genetic optimization and a modified version ofthe direct search method of optimization called the Shuffled Box Complex method ofconstrained optimization. The resulting optimal management model obtained by linkingmacro-level simulation models with efficient optimization models is capable ofidentifying optimal management strategies for an urban watershed to satisfy waterquality and economic related objectives. Finally, the optimal management model isapplied to a real world urban watershed to evaluate management strategies for waterquality management leading to the selection of near-optimal strategies

Development and Applications of Self-learning Simulation in Finite Element Analysis

Numerical analysis such as the finite element analysis (FEA) have been widely used to solve many engineering problems. Constitutive modelling is an important component of any numerical analysis and is used to describe the material behaviour. The accuracy and reliability of numerical analysis is greatly reliant on the constitutive model that is integrated in the finite element code. In recent years, data mining techniques such as artificial neural network (ANN), genetic programming (GP) and evolutionary polynomial regression (EPR) have been employed as alternative approach to the conventional constitutive modelling. In particular, EPR offers great advantages over other data mining techniques. However, these techniques require a large database to learn and extract the material behaviour. On the other hand, the link between laboratory or field tests and numerical analysis is still weak and more investigation is needed to improve the way that they matched each other. Training a data mining technique within the self-learning simulation framework is currently considered as one of the solutions that can be utilised to accurately represent the actual material behaviour. In this thesis an EPR based machine learning technique is utilised in the heart of the self-learning framework with an automation process which is coded in MATLAB environment. The methodology is applied to simulate different material behaviour in a number of structural and geotechnical applications. Two training strategies are used to train the EPR in the developed framework, total stress-strain and incremental stress-strain strategies. The results show that integrating EPR based models in the framework allows to learn the material response during the self-learning process and provide accurate predictions to the actual behaviour. Moreover, for the first time, the behaviour of a complex material, frozen soil, is modelled based on the EPR approach. The results of the EPR model predictions are compared with the actual data and it is shown that the proposed model can capture and reproduce the behaviour of the frozen soil with a very high accuracy. The developed EPR based self-learning methodology presents a unified approach to material modelling that can also help the user to gain a deeper insight into the behaviour of the materials. The methodology is generic and can be extended to modelling different engineering materials

Symbolic Regression Approaches for the Direct Calculation of Pipe Diameter

This study provides novel and accurate symbolic regression-based solutions for the calculation of pipe diameter when flow rate and pressure drop (head loss) are known, together with the length of the pipe, absolute inner roughness of the pipe, and kinematic viscosity of the fluid. PySR and Eureqa, free and open-source symbolic regression tools, are used for discovering simple and accurate approximate formulas. Three approaches are used: (1) brute force of computing power, which provides results based on raw input data; (2) an improved method where input parameters are transformed through the Lambert W-function; (3) a method where the results are based on inputs and the Colebrook equation transformed through new suitable dimensionless groups. The discovered models were simplified by the WolframAlpha simplify tool and/or the equivalent Matlab Symbolic toolbox. Novel models make iterative calculus redundant; they are simple for computer coding while the relative error remains lower compared with the solution through nomograms. The symbolic-regression solutions discovered by brute force computing power discard the kinematic viscosity of the fluid as an input parameter, implying that it has the least influence

Oil price forecasting using gene expression programming and artificial neural networks

This study aims to forecast oil prices using evolutionary techniques such as gene expression programming (GEP) and artificial neural network (NN) models to predict oil prices over the period from January 2, 1986 to June 12, 2012. Autoregressive integrated moving average (ARIMA) models are employed to benchmark evolutionary models. The results reveal that the GEP technique outperforms traditional statistical techniques in predicting oil prices. Further, the GEP model outperforms the NN and the ARIMA models in terms of the mean squared error, the root mean squared error and the mean absolute error. Finally, the GEP model also has the highest explanatory power as measured by the R-squared statistic. The results of this study have important implications for both theory and practice

암석의 기계굴착 성능 예측을 위한 유전자발현프로그래밍과 입자군집최적화에 기초한 혼합형 진화 계산 알고리즘

학위논문(박사) -- 서울대학교대학원 : 공과대학 에너지시스템공학부, 2022. 8. Seokwon Jeon.암반 기계 굴착 기술의 발전으로 기존의 발파 공법이 아닌 기계 굴착을 사용하여 지하 공간을 건설하는 사례가 증가하고 있다. 기계식 암석 굴착 분야에는 다양한 변수 간의 관계에 대한 상당한 수의 결정론적 해법이 있지만, 많은 경우 변수 간의 결정적 관계를 설정하는 것은 극히 어렵다. 그 결과 많은 연구자들이 회귀 분석을 사용하여 이러한 관계를 설명하려고 한다. 암석 파쇄 현상의 복잡하고 비선형적인 특성으로 인해 기존의 함수 피팅 기법에서 요구하는 통계 데이터에 부합하는 비선형 함수의 형태를 합리적으로 결정하기가 쉽지 않다. 따라서 본 연구에서는 기계 굴착 분야의 문제점을 해결하기 위해 유전자 발현 프로그래밍(GEP)과 입자 군집 최적화 (PSO)의 조합을 데이터 분석에 사용하였다. GEP 및 PSO는 진화적 계산 기술이며 GEP-PSO 알고리즘을 통해 데이터 세트에 맞는 비선형 함수의 형식과 상수를 자동으로 찾을 수 있다. 본 연구에서는 임팩트 해머에 대한 성능 예측 모델, 픽커터에 필요한 비에너지 예측 모델, 픽커터에 작용하는 절삭력, 수직력, 횡방향력 예측 모델을 개발하기 위해 알고리즘을 사용하였다. 모든 경우에 GEP-PSO 알고리즘을 사용하여 생성된 결과는 다중 선형 회귀에 의해 생성된 결과와 비교하여 상당히 높은 예측 정확도를 생성함을 확인하였다. 가능한 경우 GEP-PSO 알고리즘에 의해 생성된 결과와 다른 연구자가 개발한 예측 모델을 비교하여 현재 연구 과정에서 개발된 모델의 장점을 보여 줄 것으로 보인다. 높은 수준의 정확도 외에도 GEP-PSO 알고리즘을 사용하여 개발된 모델은 기존 예측 모델의 단점을 상당 부분 극복할 수 있다. 개발된 모델은 얻기 쉬운 입력 매개변수를 거의 요구하지 않으면서 더 많은 신뢰성 및 정확도를 제공하거나 기존 예측 모델에서 무시되었던 중요한 입력 매개변수를 포함하므로 더 유리하다고 볼 수 있다.With the advances in mechanical excavation technology, increasing number of underground spaces are built using mechanical excavation rather than the conventional drilling and blasting method. In the field of mechanical rock excavation, there are a fair number of deterministic solutions for the relations between different variables. However, in many cases, establishing such a relation is extremely difficult. As a result, many researchers try to explain those relations using regression analysis. Due to the complex and non-linear nature of rock cutting phenomenon, it is not easy to reasonably determine the form of the non-linear functions that fit to the statistical data as it is required by the conventional non-linear function fitting techniques. As a result, a combination of Gene Expression Programming (GEP) and Particle Swarm Optimization (PSO) was used for data analysis in this study in order to solve problems in the field of mechanical excavation. GEP and PSO are evolutionary computation techniques and the GEP-PSO algorithm is capable of automatically finding the form and constants of a non-linear function that fits on a data set. The algorithm was used in order to develop a performance prediction model for impact hammer, a prediction model for specific energy required by point attack picks, and models for prediction of cutting, normal, and side force acting on a point attack pick. In all cases, the results generated using the GEP-PSO algorithm produced significantly high prediction accuracy in comparison to those generated by multiple linear regression. When possible, comparisons were made between the results generated by the GEP-PSO algorithm and the prediction models developed by other researchers to show the advantages of the models developed over the course of the present study. In addition to high level of accuracy, the models developed using GEP-PSO algorithm could overcome shortcomings of the existing prediction models to a fair extent. The developed models are more advantageous as they provide more reliability/accuracy while requiring few easy-to-obtain input parameters, and/or they include the significant input parameters that have been neglected by the existing prediction models.1. Introduction 1 2. Literature Review 10 2.1 Impact hammer performance prediction 10 2.1.1 Existing performance prediction models 11 2.1.2 Performance prediction model 13 2.2 Specific energy prediction 14 2.2.1 Parameters with a significant impact on specific energy 16 2.2.2 Specific energy prediction model 22 2.3 Forces acting on a point attack pick 22 2.3.1 Existing force prediction models 23 2.3.2 Parameters with a significant impact on forces 29 2.3.3 Forces prediction models 30 3. Statistical Data 31 3.1 Impact hammer performance 31 3.1.1 Levent-Hisarustu tunnel 31 3.1.2 Uskudar-Cekmekoy tunnel 33 3.2 Specific energy required by point attack picks 37 3.3 Forces applied on point attack picks 41 4. Data Analysis Method 43 4.1 Gene Expression Programming (GEP) 45 4.1.1 Genetic Operators 47 4.1.2 The Basic Flowchart of GEP algorithm 55 4.2 Particle Swarm Optimization (PSO) 56 4.3 GEP-PSO algorithm 58 5. Results and Discussion 64 5.1 The suggested impact hammer performance prediction model 65 5.2 The model suggested for prediction of specific energy required by point attack picks 75 5.3 The suggested models for prediction of forces acting on a point attack pick 88 6. Conclusions 97 6.1 Performance prediction model for impact hammer 97 6.2 Prediction model for specific energy required by point attack picks 99 6.3 Models for prediction of cutting, normal, and side force acting on a point attack pick 100 References 102 초 록 116 Appendix A 118 Acknowledgment 138박

Modelling of geotechnical structures using multi-variate adaptive regression spline (MARS) and genetic programming (GP)

The soil is considered as a complex material produced by the weathering of solid rock. Due to its uncertain behavior, modeling the behavior of such materials is complex by using more traditional forms of mechanistic based engineering methods like analytical and finite element methods etc. Very often it is difficult to develop theoretical/statistical models due to the complex nature of the problem and uncertainty in soil parameters. These are situations where data driven approach has been found to more appropriate than model oriented approach. To take care of such problems in artificial intelligence (AI) techniques has been developed in the computational methods. Though AI techniques has proved to have the superior predictive ability than other traditional methods for modeling complex behavior of geotechnical engineering materials, still it is facing some criticism due to the lack of transparency, knowledge extraction and model uncertainty. To overcome this problem there are developments of improvised AI techniques. Different AI techniques as ‘black box’ i.e artificial neural network (ANN), ‘grey box’ i.e Genetic programming (GP) and ‘white box’ i.e multivariate adaptive regression spline (MARS) depending upon its transparency and knowledge extraction. Here, in this study of GP and MARS ‘grey box’ and ‘white box’ AI techniques are applied to some geotechnical problems such as prediction of lateral load capacity of piles in clay, pull-out capacity of ground anchor, factor of safety of slope stability analysis and ultimate bearing capacity of shallow foundations.. Different statistical criteria are used to compare the developed GP and MARS models with other AI models like ANN and support vector machine (SVM) models. It was observed that for the problems considered in the present study, the MARS and GP model are found to be more efficient than ANN and SVM model and the model equations are also found to be more comprehensive