Application of hybrid intelligent systems in predicting the unconfined compressive strength of clay material mixed with recycled additive

A reliable prediction of the soil properties mixed with recycled material is considered as an ultimate goal of many geotechnical laboratory works. In this study, after planning and conducting a series of laboratory works, some basic properties of marine clay treated with recycled tiles together with their unconfined compressive strength (UCS) values were obtained. Then, these basic properties were selected as input variables to predict the UCS values through the use of two hybrid intelligent systems i.e., the neuro-swarm and the neuro-imperialism. Actually, in these systems, respectively, the weights and biases of the artificial neural network (ANN) were optimized using the particle swarm optimization (PSO) and imperialism competitive algorithm (ICA) to get a higher accuracy compared to a pre-developed ANN model. The best neuro-swarm and neuro-imperialism models were selected based on several parametric studies on the most important and effective parameters of PSO and ICA. Afterward, these models were evaluated according to several well-known performance indices. It was found that the neuro-swarm predictive model provides a higher level of accuracy in predicting the UCS of clay soil samples treated with recycled tiles. However, both hybrid predictive models can be used in practice to predict the UCS values for initial design of geotechnical structures

Application of several optimization techniques for estimating TBM advance rate in granitic rocks

https://www.sciencedirect.com/science/article/pii/S1674775518303056This study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine (TBM) in different weathered zones of granite. For this purpose, extensive field and laboratory studies have been conducted along the 12,649 m of the Pahang – Selangor raw water transfer tunnel in Malaysia. Rock properties consisting of uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), rock mass rating (RMR), rock quality designation (RQD), quartz content (q) and weathered zone as well as machine specifications including thrust force and revolution per minute (RPM) were measured to establish comprehensive datasets for optimization. Accordingly, to estimate the advance rate of TBM, two new hybrid optimization techniques, i.e. an artificial neural network (ANN) combined with both imperialist competitive algorithm (ICA) and particle swarm optimization (PSO), were developed for mechanical tunneling in granitic rocks. Further, the new hybrid optimization techniques were compared and the best one was chosen among them to be used for practice. To evaluate the accuracy of the proposed models for both testing and training datasets, various statistical indices including coefficient of determination (R2), root mean square error (RMSE) and variance account for (VAF) were utilized herein. The values of R2, RMSE, and VAF ranged in 0.939–0.961, 0.022–0.036, and 93.899–96.145, respectively, with the PSO-ANN hybrid technique demonstrating the best performance. It is concluded that both the optimization techniques, i.e. PSO-ANN and ICA-ANN, could be utilized for predicting the advance rate of TBMs; however, the PSO-ANN technique is superior

Evaluation of Induced Settlements of Piled Rafts in the Coupled Static-Dynamic Loads Using Neural Networks and Evolutionary Polynomial Regression

Coupled Piled Raft Foundations (CPRFs) are broadly applied to share heavy loads of superstructures between piles and rafts and reduce total and differential settlements. Settlements induced by static/coupled static-dynamic loads are one of the main concerns of engineers in designing CPRFs. Evaluation of induced settlements of CPRFs has been commonly carried out using three-dimensional finite element/finite difference modeling or through expensive real-scale/prototype model tests. Since the analyses, especially in the case of coupled static-dynamic loads, are not simply conducted, this paper presents two practical methods to gain the values of settlement. First, different nonlinear finite difference models under different static and coupled static-dynamic loads are developed to calculate exerted settlements. Analyses are performed with respect to different axial loads and pile’s configurations, numbers, lengths, diameters, and spacing for both loading cases. Based on the results of well-validated three-dimensional finite difference modeling, artificial neural networks and evolutionary polynomial regressions are then applied and introduced as capable methods to accurately present both static and coupled static-dynamic settlements. Also, using a sensitivity analysis based on Cosine Amplitude Method, axial load is introduced as the most influential parameter, while the ratio l/d is reported as the least effective parameter on the settlements of CPRFs

A review: evolutionary computations (GA and PSO) in geotechnical engineering

This study briefly reviews the application of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) in geotechnical engineering since GA and PSO are widely used in civil engineering. The application of GA and PSO is studied in three popular families of geotechnical problems including unconfined seepage analysis, slope stability analysis, and foundation design. In each category the available results from different studies are reviewed and compared. The comparison of results shows the desirable accuracy in the predicting of optimal values in the process of analysis and design. The presented methods perform successfully in the reviewed problems. However, PSO predicts the optimum values in fewer numbers of iterations, which suggests higher performance in term of implementation/application

Artificial Neural Network-Cuckoo Optimization Algorithm (ANN-COA) for Optimal Control of Khorramabad Wastewater Treatment Plant, Iran

In this study a hybrid estimation model ANN-COA developed to provide an accurate prediction of a Wastewater Treatment Plant (WWTP). An effective strategy for detection of some output parameters tested on a hardware setup in WWTP. This model is designed utilizing Artificial Neural Network (ANN) and Cuckoo Optimization Algorithm (COA) to improve model performances; which is trained by a historical set of data collected during a 6 months operation. ANN-COA based on the difference between the measured and simulated values, allowed a quick revealing of the faults. The method could obtain the fault detection and used in solving continuous and discrete optimization problems, successfully. After constructing and modelling the method, selected performance indices including coefficient of Regression, Mean-Square Error, Root-Mean-Square Error and Aggregated Measure used to compare the obtained results. This analysis revealed that the hybrid ANN-COA model offers a higher degree of accuracy for predicting and control the WWTP

Training Multilayer Perceptron with Genetic Algorithms and Particle Swarm Optimization for Modeling Stock Price Index Prediction

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Traffic Flow Forecasting for Road Tunnel Using PSO-GPR Algorithm with Combined Kernel Function

With the rapid development of long or extra-long highway tunnel, accurate and reliable methods and techniques to forecast traffic flow for road tunnel are urgently needed to improve the ventilation efficiency and saving energy. This paper presents a new hybrid Gaussian process regression (GPR) optimized by particle swarm optimization (PSO) for coping with the forecasting of the uncertain, nonlinear, and complex traffic flow for road tunnel. In this proposed coupling approach, the PSO algorithm is employed to overcome the disadvantages of too strong dependence of optimization effect on initial value and easy falling into local optimum of the traditional conjugate gradient algorithm and accurately search the optimal hyperparameters of the GPR method, and the GPR model simulates the internal uncertainties and dynamic feature of tunnel traffic flow. The predicted results indicate that the proposed PSO-GPR algorithm with different kernel function is able to predict traffic flow for road tunnel with a higher degree of accuracy. The PSO-GPR-CK is effective in boosting the forecasting accuracy in comparison with the single kernel function and is worth promoting in the field of traffic flow forecasting for road tunnel to improve the ventilation efficiency

Rock mass classification for predicting environmental impact of blasting on tropically weathered rock

Tropical climate and post tectonic impact on the rock mass cause severe and deep weathering in complex rock formations. The uniqueness of tropical influence on the geoengineering properties of rock mass leads to significant effects on blast performance especially in the developmental stage. Different rock types such as limestone and granite exhibit different weathering effects which require special attention for classifying rock mass for blastability purpose. Rock mass classification systems have been implemented for last century for various applications to simplify complexity of rock mass. Several research studies have been carried out on rock mass and material properties for five classes of weathered rock- fresh, slightly, moderately, highly and completely weathered rock. There is wide variation in rock mass properties- heterogeneity and strength of weathered rocks in different weathering zones which cause environmental effects due to blasting. Several researchers have developed different techniques for prediction of air overpressure (AOp), peak particle velocity (PPV) and flyrock primarily for production blast. These techniques may not be suitable for prediction of blast performance in development benches in tropically weathered rock mass. In this research, blast monitoring program were carried out from a limestone quarry and two granite quarries. Due to different nature of properties, tropically weathered rock mass was classified as massive, blocky and fractured rock for simpler evaluation of development blast performance. Weathering Index (WI) is introduced based on porosity, water absorption and Point Load Index (PLI) strength properties of rock. Weathering index, porosity index, water absorption index and point load index ratio showed decreasing trend from massive to fractured tropically weathered rock. On the other hand, Block Weathering Index (BWI) was developed based on hypothetical values of exploration data and computational model. Ten blasting data sets were collected for analysis with blasting data varying from 105 to 166 per data set for AOp, PPV and flyrock. For granite, one data set each was analyzed for AOp and PPV and balance five data sets were analyzed for flyrock in granite by variation in input parameters. For prediction of blasting performance, varied techniques such as empirical equations, multivariable regression analysis (MVRA), hypothetical model, computational techniques (artificial intelligence-AI, machine learning- ML) and graphical charts. Measured values of blast performance was also compared with prediction techniques used by previous researchers. Blastability Index (BI), powder factor, WI are found suitable for prediction of all blast performance. Maximum charge per delay, distance of monitoring point are found to be critical factors for prediction of AOp and PPV. Stiffness ratio is found to be a crucial factor for flyrock especially during developmental blast. Empirical equations developed for prediction of PPV in fractured, blocky, and massive limestone showed R2 (0.82, 0.54, and 0.23) respectively confirming that there is an impact of weathering on blasting performance. Best fit equation was developed with multivariable regression analysis (MVRA) with measured blast performance values and input parameters. Prediction of flyrock for granite with MVRA for massive, blocky and fractured demonstrated R2 (0.8843, 0.86, 0.9782) respectively. WI and BWI were interchangeably used and results showed comparable results. For limestone, AOp analysed with model PSO-ANN showed R2(0.961); PPV evaluated with model FA-ANN produced R2 (0.966). For flyrock in granite with prediction model GWO-ANFIS showed R2 (1) The same data set was analysed by replacing WI with BWI showed equivalent results. Model ANFIS produced R2 (1). It is found the best performing models were PSO-ANN for AOp, FA-ANN for PPV and GWO-ANFIS for flyrock. Prediction charts were developed for AOp, PPV and flyrock for simple in use by site personnel. Blastability index and weathering index showed variation with reclassified weathering zones – massive, blocky and fractured and they are useful input parameters for prediction of blast performance in tropically weathered rock

수중 유기오염물질 제거를 위한 금속-유기 복합체 MIL-100(Fe)의 합성 및 제거 특성 연구

학위논문(석사) -- 서울대학교대학원 : 농업생명과학대학 생태조경.지역시스템공학부(지역시스템공학전공), 2021.8. 김성배.The aim of this study was to characterize the removal of contaminants from aqueous solution using a Metal-organic framework (MOF). The MOF is a porous crystalline complex made by a strong coordination bond between a metal cluster and an organic linker, which has large surface area, structural flexibility. MIL-100(Fe) was synthesized at room temperature with Iron (Fe) and Trimesic acid (H3BTC). MIL-100(Fe) has environmental-friendly nature, high water stability, and great adsorption capacity. In this study, the MIL-100(Fe) was applied as an adsorbent to removal of Rhodamine B (RhB) and Diclofenac (DCF) from aqueous solution. Batch experiments were conducted for RhB and DCF, respectively under single-parameter and multi-parameter experiment conditions. The maximum adsorption capacity for RhB is 61.845 mg g-1 and DCF is 414. 581 mg g-1. The main mechanisms are π-π interaction and electrostatic attraction for RhB removal, and π-π interaction and hydrogen bonding for DCF removal. Further, Response surface methodology (RSM) and Artificial neural network (ANN) were employed to model and optimized the RhB and DCF removal in the range of the CCD matrix as multi-parameter models. In RSM modeling, the cubic regression model was developed for RhB removal and the regressor variable of pH had a larger coefficient value indicating that pH had a highest impact on the RhB removal rate. The optimum RhB removal rate was found at pH 5.3, adsorbent dose 2.0 g L-1, initial RhB concentration 73 mg L-1 through the prediction of the modeled ANN with topology 3:8:1. The optimum DCF removal rate was found at initial pH 6.1, adsorbent dose 0.5 g L-1, initial DCF concentration 63 mg L-1, temperature 22 ℃ through the prediction of the modeled ANN with topology 4:7:6:2. Study results indicate that the MIL-100(Fe) synthesized at room temperature shows high adsorption capacity for RhB and DCF removal from synthetic water, and the RSM and ANN model could be successfully optimize and predict for RhB and DCF removal as multi-parameter models.본 연구의 목적은 금속-유기 복합체 (Metal organic framework, MOF)를 사용하여 수용액에서 오염 물질을 제거하는 것이다. MOF는 금속 클러스터와 유기 링커 사이의 강력한 배위 결합으로 만들어진 다공성 결정형 복합체로, 표면적이 크고 구조적 유연성이 있다는 특성을 가지고 있다. MOF 중에서도 상온에서 합성한 MIL-100(Fe)는 철 (Fe)과 Trimesic acid 를 사용하였고, 환경 친화적이며, 수중 안정성이 높고, 높은 흡착효율을 보이는 흡착제이다. 본 연구에서는 MIL-100(Fe)를 흡착제로 사용하여 수중의Rhodamine B (RhB)와 Diclofenac (DCF)를 제거하였다. 단일 매개 변수 및 다중 매개 변수 실험 조건에서 RhB와 DCF 각각에 대해 흡착 회분 실험을 진행하였다. MIL-100(Fe)을 이용한 RhB의 최대흡착능은 61.845 mg g-1 이고, DCF의 최대흡착능은 414.581 mg g-1 이다. pH 실험 결과, RhB 흡착의 주된 메커니즘은 π-π 결합과 정전기적 인력이며, DCF 흡착의 주된 메커니즘은 π-π 결합과 수소결합이다. 또한, 반응표면방법론 (RSM)과 인공신경망 (ANN)을 사용하여 중심합성설계 (CCD) 매트릭스 조건 범위에서 RhB와 DCF 제거에 대한 다중 매개 변수 실험을 모델링하고 최적화하였다. RSM 모델링에서는 RhB 제거를 위해 3차 회귀 모델이 사용되었으며, 변수 중에서 가장 큰 회귀 변수 값을 갖는 pH가 RhB 제거율에 가장 큰 영향을 미친다는 것을 나타낸다. ANN 모델링을 통한 최적의 RhB 제거율을 보이는 조건은 3:8:1 의 ANN 구조에서 pH 5.3, 흡착제 용량 2.0 g L-1, 초기 RhB 농도 73 mg L-1 이다. 최적의 DCF 제거율을 보이는 조건은 4:7:6:2 의 ANN 구조에서 초기 pH 6.1, 흡착제 용량 0.5 g L-1, 초기 DCF 농도 63 mg L-1, 반응온도 22 ℃ 이다. 본 연구 결과를 통해 상온에서 합성한 MIL-100(Fe)이 수중 RhB와 DCF를 제거에 높은 흡착능을 보이는 효과적인 흡착제임을 확인하였고, RSM과 ANN 모델이 다중 매개 변수 모델로서 RhB와 DCF 제거를 최적화하고 예측하는 데 효과적인 모델임을 확인하였다.1. Introduction 1 1.1. Background 1 1.1.1. Metal-organic framework (MOF) 3 1.1.2. Contaminants 6 1.1.3. Multi-parameter model 12 1.2. Objective 14 2. Literature Review 15 2.1. Adsorption of contaminants from aqueous solution using MOFs 15 2.2. Dye adsorption using MOFs 18 2.3. Pharmaceutical adsorption using MOFs 23 3. Materials and Methods 30 3.1. Synthesis of MIL-100(Fe) at room temperature 30 3.2. Characterization of MIL-100(Fe) 33 3.3. RhB adsorption from synthetic water 35 3.3.1. Single-parameter experiments for RhB removal 35 3.3.2. Multi-parameter experiments for RhB removal 39 3.4. DCF adsorption from synthetic water 43 3.4.1. Single-parameter experiments for DCF removal 43 3.4.2. Multi-parameter experiments for DCF removal 48 3.5. Data analysis for single-parameter experiments 51 3.6. Multi-parameter modeling through RSM and ANN 54 3.6.1. Response surface methodology (RSM) 54 3.6.2. Artifical neural network (ANN) 56 4. Results and Discussion 60 4.1. Characterization of MIL-100(Fe) 60 4.2. Adsorption studies for RhB 72 4.2.1. Single-parameter experiments for RhB removal 72 4.2.2. Multi-parameter modeling using RSM 80 4.2.3. Multi-parameter modeling using ANN 87 4.3. Adsorption studies for DCF 99 4.3.1. Single-parameter experiments for DCF removal 99 4.3.2. Multi-parameter modeling using ANN 109 5. Conclusions 121 6. References 124석

Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles

Rock-socketed piles are commonly used in foundations built in soft ground, and thus, their bearing capacity is a key issue of universal concern in research, design and construction. The accurate prediction of the ultimate bearing capacity (Qu) of rock-socketed piles is a difficult task due to the uncertainty surrounding the various factors that affect this capacity. This study was aimed at developing an artificial neural network (ANN) model, as well as a hybrid model based on both particle swarm optimisation (PSO) and ANN, with which to predict the Qu of rock-socketed piles. PSO, a powerful population-based algorithm used in solving continuous and discrete optimisation problems, was here employed as a robust global search algorithm to determine ANN weights and biases and thereby improve model performance. To achieve the study aims, 132 piles socketed in various rock types as part of the Klang Valley Mass Rapid Transit project, Malaysia, were investigated. Based on previous related investigations, parameters with the most influence on Qu were identified and utilised in the modelling procedure of the intelligent systems. After constructing and modelling these systems, selected performance indices including the coefficient of determination (R2), root-mean-square error, variance account for and total ranking were used to identify the best models and compare the obtained results. This analysis revealed that the hybrid PSO–ANN model offers a higher degree of accuracy compared to conventional ANN for predicting the Qu of rock-socketed piles. However, the developed model would be most useful in the preliminary stages of pile design and should be used with caution