scholarly journals A Liquefaction Study Using ENN, CA, and Biogeography Optimized-Based ANFIS Technique

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Construction Projects ◽  
Fuzzy Inference ◽  
Model Development ◽  
Standard Penetration Test ◽  
Cyclic Stress ◽  
Cultural Algorithm ◽  
Liquefaction Potential ◽  
Cyclic Stress Ratio ◽  
Inference System ◽  
Reliable Technique

In any construction projects,assessment of liquefaction potential induced due to seismic excitation during earthquake is a critical concern.The objective of present model development is to classify and assess liquefaction potential of soil.This paper addresses Emotional Neural Network(ENN), Cultural Algorithm(CA) and biogeography optimized(BBO) based adaptive neuro-fuzzy inference system (ANFIS) for liquefaction study.The performance of neural emotional network and cultural algorithm has been also discussed. BBO-ANFIS combines the biogeography features to optimize the ANFIS parameters to achieve higher prediction accuracy.The model is trained with case history of liquefaction databases.Two parameters are used as input such as the cyclic stress ratio and standard penetration test (SPT) value.The performance of these models was assessed using different indexes i.e. sensitivity, specificity, FNR, FPR and accuracy rate.The performance of all models is compared.Among the models, the BBO-ANFIS model has been outperformed and can be adopted as new reliable technique for liquefaction study.

Regression model for evaluating liquefaction potential by discriminant analysis of the SPT N value

2005 ◽  
Vol 42 (3) ◽  
pp. 856-875 ◽  
Author(s):  
Sheng-Yao Lai ◽  
Ping-Sien Lin ◽  
Ming-Jyh Hsieh ◽  
Hoi-Fung Jim
Keyword(s):  
Discriminant Analysis ◽  
Standard Penetration Test ◽  
Cyclic Stress ◽  
Soil Liquefaction ◽  
Liquefaction Resistance ◽  
Multivariate Statistical ◽  
Liquefaction Potential ◽  
Cyclic Stress Ratio ◽  
N Value ◽  
Spt N Value

Discriminant models are developed for evaluating soil liquefaction potential, using standard penetration test (SPT) data for 592 occurrences of liquefaction and nonliquefaction. The discriminant model used is a multivariate statistical method. The square root of the SPT N value, (N1)601/2, and the logarithm of the cyclic stress ratio, ln CSR7.5, are adopted as the major parameters for analyses. Two models measuring liquefaction resistance through the SPT N value are also established in this study, which allows calculated results to be compared with the empirical curves. Key words: liquefaction, discriminant analysis, misclassified probability.

scholarly journals IDENTIFYING KEY FACTORS OF COMMUTER TRAIN SERVICE QUALITY:AN EMPIRICAL ANALYSIS FOR DHAKA CITY

2019 ◽  
Vol 31 (2) ◽  
Author(s):  
Anika Nowshin Mowrin ◽  
Md. Hadiuzzaman ◽  
Saurav Barua ◽  
Md. Mizanur Rahman
Keyword(s):  
Traffic Congestion ◽  
Fuzzy Inference ◽  
Confusion Matrix ◽  
Model Development ◽  
Model Performance ◽  
Total Sample ◽  
Dhaka City ◽  
Anfis Model ◽  
Regular User ◽  
Inference System

Commuter train is a viable alternative to road transport to ease the traffic congestion which requires appropriate planning by concerned authorities. The research is aimed to assess passengers’ perception about commuter train service running in areas near Dhaka city. An Adaptive Neuro Fuzzy Inference System (ANFIS) model has been developed to evaluate service quality (SQ) of commuter train. Field survey data has been conducted among 802 respondents who were the regular user of commuter train and 12 attributes have been selected for model development. ANFIS was developed by the training and then tested by 80% and 20% of the total sample respectively. After that, model performance has been evaluated by (i) Confusion Matrix (ii) Root Mean Square Error (RMSE) and attributes are ranked based on their relative importance. The proposed ANFIS model has 61.50% accuracy in training and 47.80% accuracy in testing.  From the results, it is found that 'Bogie condition', 'Cleanliness', ‘Female harassment’, 'Behavior of staff' and 'Toilet facility' are the most significant attributes. This indicates that some necessary measures should be taken immediately to recover the effects of these attributes to improve the SQ of commuter train. 

scholarly journals Fuzzy Approach in Rail Track Degradation Prediction

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Mostafa Karimpour ◽  
Lalith Hitihamillage ◽  
Najwa Elkhoury ◽  
Sara Moridpour ◽  
Reyhaneh Hesami
Keyword(s):  
Prediction Model ◽  
Human Error ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Model Development ◽  
Rail Transport ◽  
Maintenance Cost ◽  
Inference System ◽  
Rail Track

Rail transport authorities around the world have been facing a significant challenge when predicting rail infrastructure maintenance work. With the restrictions on financial support, the rail transport authorities are in pursuit of improved modern methods, which can provide a precise prediction of rail maintenance timeframe. The expectation from such a method is to develop models to minimise the human error that is strongly related to manual prediction. Such models will help rail transport authorities in understanding how the track degradation occurs at different conditions (e.g., rail type, rail profile) over time. They need a well-structured technique to identify the precise time when rail tracks fail to minimise the maintenance cost/time. The rail track characteristics that have been collected over the years will be used in developing a degradation prediction model for rail tracks. Since these data have been collected in large volumes and the data collection is done both electronically and manually, it is possible to have some errors. Sometimes these errors make it impossible to use the data in prediction model development. An accurate model can play a key role in the estimation of the long-term behaviour of rail tracks. Accurate models can increase the efficiency of maintenance activities and decrease the cost of maintenance in long-term. In this research, a short review of rail track degradation prediction models has been discussed before estimating rail track degradation for the curves and straight sections of Melbourne tram track system using Adaptive Network-based Fuzzy Inference System (ANFIS) model. The results from the developed model show that it is capable of predicting the gauge values with R2 of 0.6 and 0.78 for curves and straights, respectively.

scholarly journals A SIMPLE SHAKING TABLE TEST TO MEASURE LIQUEFACTION POTENTIAL OF PRAMBANAN AREA, YOGYAKARTA, INDONESIA

2021 ◽  
Vol 11 (3) ◽  
pp. 89-108
Author(s):  
Lindung Zalbuin Mase ◽  
Teuku Faisal Fathani ◽  
Agus Darmawan Adi
Keyword(s):  
Dynamic Load ◽  
Stress Ratio ◽  
Shaking Table Test ◽  
Pore Water Pressure ◽  
Water Pressure ◽  
Pressure Ratio ◽  
Cyclic Stress ◽  
Shaking Table ◽  
Liquefaction Potential ◽  
Cyclic Stress Ratio

This paper presents the experimental study of liquefaction potential for sandy soil in Prambanan Area, Yogyakarta, Indonesia, which underwent liquefaction due to the Mw 6.3 Jogja Earthquake on May 27, 2006. Shaking table tests considering the variation of acceleration and shaking duration were performed to investigate the liquefaction potential of sand. The liquefaction time stages including time to start liquefaction, time to start pore pressure dissipation, and liquefaction duration were observed. The percentage of liquefaction duration increase, the excess pore water pressure ratio and the required time to generate liquefaction, and the effect of applied acceleration to cyclic stress ratio, were also presented. The results showed that the sand could undergo liquefaction under the variation of dynamic load. The variation of dynamic load significantly influenced the time stages of liquefaction, the increase of liquefaction duration percentage and cyclic stress ratio. The results also exhibited that the larger applied acceleration and the longer shaking duration means the longer liquefaction duration and the larger liquefaction potential. In general, the result could bring the recommendation to the liquefaction countermeasure for Prambanan Area.

scholarly journals ANALISIS POTENSI CYCLIC MOBILITY PADA TANAH KOHESIF

2019 ◽  
Vol 2 (3) ◽  
pp. 77
Author(s):  
Nicky Fernando ◽  
Aniek Prihatiningsih
Keyword(s):  
Axial Strain ◽  
Cyclic Strain ◽  
Standard Penetration Test ◽  
Cyclic Stress ◽  
Penetration Test ◽  
Cone Penetration ◽  
Cyclic Stress Ratio ◽  
Cyclic Mobility ◽  
Cyclic Resistance ◽  
Cyclic Resistance Ratio

Likuifaksi merupakan fenomena dimana kekuatan tahanan tanah berkurang karena meningkatnya tegangan air pori saat gempa bumi berlangsung. Likuifaksi dibagi menjadi dua tipe berdasarkan proses kejadiannya yaitu flow liquefaction dan cyclic mobility. Hal pertama dalam analisis potensi likuifaksi adalah pemeriksaan kerentanan likuifaksi dari karakteristik tanah. Pemeriksaan kerentanan menggunakan empat metode yaitu Chinese criteria, metode Seed et al. dan metode Bray dan Sancio. Jika tanah menunjukan rentan terhadap likuifaksi, perhitungan evaluasi dapat dilanjutkan jika tidak maka perhitungan tidak dilanjutkan. Setelah menentukan kerentanan, tanah yang rentan likuifaksi akan ditentukan tipe likuifaksi menggunakan state criteria. Penentuan tipe likuifaksi dapat dilihat dari grafik hubungan deviatoric stress (q), mean effective stress (p’) dan axial strain (εa). Evaluasi potensi likuifaksi menggunakan metode cyclic strain approach. Metode ini menggunakan dua variabel yaitu cyclic stress ratio (CSR) dan cyclic resistance ratio (CRR) yang dapat ditentukan dari data tes lapangan untuk menentukan potensi likuifaksi setiap lapisan tanah. Tes lapangan yang digunakan adalah standard penetration test (SPT) dan cone penetration test (CPT). Penelitian ini menganalisa potensi cyclic mobility pada tanah kohesif serta faktor keamanan. Hasil dari penelitian ini menunjukan bahwa tipe likuifaksi yang terjadi adalah cyclic mobility dan adanya potensi likuifaksi pada tanah kohesif.

Making decision toward overseas construction projects

2019 ◽  
Vol 26 (2) ◽  
pp. 285-302 ◽  
Author(s):  
Wahyudi P. Utama ◽  
Albert P.C. Chan ◽  
Hafiz Zahoor ◽  
Ran Gao ◽  
Dwifitra Y. Jumas
Keyword(s):  
Decision Support ◽  
Construction Projects ◽  
Fuzzy Inference ◽  
Optimization Method ◽  
Preliminary Assessment ◽  
Anfis Model ◽  
Data Set ◽  
Inference System ◽  
Content Type ◽  
Neuro Fuzzy

Purpose The purpose of this paper is to introduce a decision support aid for deciding an overseas construction project (OCP) using an adaptive neuro fuzzy inference system (ANFIS). Design/methodology/approach This study presents an ANFIS approach as a decision support aid for assessment of OCPs. The processing data were derived from 110 simulation cases of OCPs. In total, 21 international factors observed from a Delphi survey were determined as assessment variables to examine the cases. The experts were involved to evaluate and judge whether the company should Go or Not Go for an OCP, based on the different parameter scenarios given. To measure the performance of the ANFIS model, root mean square error (RMSE) and coefficient of correlation (R) were employed. Findings The result shows that optimum ANFIS model indicating RMSE and R scores adequately near between 0 and 1, respectively, was obtained from parameter set of network algorithm with two input membership functions, Gaussian type of membership function and hybrid optimization method. When the model tested to nine real OCPs data, the result indicates 88.89 percent accurate. Research limitations/implications The use of simulation cases as data set in development the model has several advantages. This technique can be replicated to generate other case scenarios which are not available publicly or limited in terms of quantity. Originality/value This study evidences that the developed ANFIS model can predict the decision satisfactorily. Therefore, it can help companies’ management to make preliminary assessment of an OCP.

scholarly journals Performance prediction of construction projects using soft computing methods

2019 ◽  
Vol 46 (7) ◽  
pp. 609-620 ◽  
Author(s):  
Seyedeh Sara Fanaei ◽  
Osama Moselhi ◽  
Sabah T. Alkass
Keyword(s):  
Performance Prediction ◽  
Construction Projects ◽  
Fuzzy Inference ◽  
Performance Indicator ◽  
Relative Weight ◽  
Computing Methods ◽  
Subtractive Clustering ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Project Data

Key performance indicators (KPIs) evaluate different aspects of projects and are used to determine the health status of projects. While there is considerable work on project quantitative performance prediction, less attention, however, has been directed towards qualitative performance prediction. This paper offers a novel framework for qualitatively measuring and predicting six important construction project KPIs using the neuro-fuzzy technique. Neuro-fuzzy models are developed to map the KPIs of three critical project stages to whole project KPIs. Subtractive clustering is utilized to automatically generate initial fuzzy inference system (FIS) models and the artificial neural network (ANN) technique is used to tune the parameters of the initial FIS models. The relative weight of each KPI is determined using a series of computing methods namely, analytic hierarchy process (AHP) and genetic algorithm (GA), to generate the performance indicator (PI). The developed models are validated with real project data showing that the rate of error is reasonably low. The results show that the AHP method is more accurate when compared to the GA method. This framework can be used in building construction projects to help decision-makers evaluate the performance of their projects.

scholarly journals Improving Estimate at Completion (EAC) Cost of Construction Projects Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

2021 ◽  
Author(s):  
Seyedeh Razieh Dastgheib ◽  
Mohammad Reza Feylizadeh ◽  
Morteza Bagherpour ◽  
Amin Mahmoudi
Keyword(s):  
Fuzzy Inference System ◽  
Construction Projects ◽  
Fuzzy Inference ◽  
Optimization Procedure ◽  
Project Performance ◽  
Vital Role ◽  
General Estimate ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Qualitative Variables

Earned Value Management (EVM) is well-known technique for measuring project performance and progress. Due to EVM's attitude to combining cost and time performance simultaneously, project performance can be forecasted accurately and this plays a vital role in the future of the projects. In the current study, the authors employed Adaptive Neuro-Fuzzy Inference System (ANFIS) as a powerful prediction tool to forecast completion cost of the projects considering the percentage of risk for qualitative variables and comparing it with other types of Neural Networks. Since the network structure is usually tuned based on the obtained results, network optimization procedure is applied using a conventional method for estimating cost-caused project breakdown. The results showed ANFIS had a suitable performance (MSE=0.0003) and based on the sensitivity analysis, EV is recognized as the most sensitive factor in the project. This paper improves the general estimate at completion formula by taking uncertain conditions into account.

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