scholarly journals Construction schedule risk analysis – a hybrid machine learning approach

2022 ◽  
Vol 27 ◽  
pp. 70-93
Author(s):  
John Patrick Fitzsimmons ◽  
Ruodan Lu ◽  
Ying Hong ◽  
Ioannis Brilakis
Keyword(s):  
Monte Carlo ◽  
Risk Analysis ◽  
Hybrid Method ◽  
Construction Projects ◽  
Gaussian Mixture ◽  
Machine Intelligence ◽  
Accurate Estimation ◽  
Support Vector ◽  
Risk Simulation ◽  
The Uk

The UK commissions about £100 billion in infrastructure construction works every year. More than 50% of them finish later than planned, causing damage to the interests of stakeholders. The estimation of time-risk on construction projects is currently done subjectively, largely by experience despite there are many existing techniques available to analyse risk on the construction schedules. Unlike conventional methods that tend to depend on the accurate estimation of risk boundaries for each task, this research aims to proposes a hybrid method to assist planners in undertaking risk analysis using baseline schedules with improved accuracy. The proposed method is endowed with machine intelligence and is trained using a database of 293,263 tasks from a diverse sample of 302 completed infrastructure construction projects in the UK. It combines a Gaussian Mixture Modelling-based Empirical Bayesian Network and a Support Vector Machine followed by performing a Monte Carlo risk simulation. The former is used to investigate the uncertainty, correlated risk factors, and predict task duration deviations while the latter is used to return a time-risk simulated prediction. This study randomly selected 10 projects as case studies followed by comparing their results of the proposed hybrid method with Monte Carlo Simulation. Results indicated 54.4% more accurate prediction on project delays.

scholarly journals Monte Carlo approach to fuzzy AHP risk analysis in renewable energy construction projects

PLoS ONE ◽  
2019 ◽  
Vol 14 (6) ◽  
pp. e0215943 ◽  
Author(s):  
Luis Serrano-Gomez ◽  
Jose Ignacio Munoz-Hernandez
Keyword(s):  
Monte Carlo ◽  
Renewable Energy ◽  
Risk Analysis ◽  
Construction Projects ◽  
Fuzzy Ahp ◽  
Monte Carlo Approach

scholarly journals Indonesian Stock Prediction using Support Vector Machine (SVM)

2018 ◽  
Vol 164 ◽  
pp. 01031 ◽  
Author(s):  
Murtiyanto Santoso ◽  
Raymond Sutjiadi ◽  
Resmana Lim
Keyword(s):  
Support Vector Machine ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Prediction Method ◽  
Gaussian Mixture ◽  
Machine Intelligence ◽  
Support Vector ◽  
Predicted Model ◽  
Python Programming ◽  
Model Support

This project is part of developing software to provide predictive information technology-based services artificial intelligence (Machine Intelligence) or Machine Learning that will be utilized in the money market community. The prediction method used in this early stages uses the combination of Gaussian Mixture Model and Support Vector Machine with Python programming. The system predicts the price of Astra International (stock code: ASII.JK) stock data. The data used was taken during 17 yr period of January 2000 until September 2017. Some data was used for training/modeling (80 % of data) and the remainder (20 %) was used for testing. An integrated model comprising Gaussian Mixture Model and Support Vector Machine system has been tested to predict stock market of ASII.JK for l d in advance. This model has been compared with the Market Cummulative Return. From the results, it is depicts that the Gaussian Mixture Model-Support Vector Machine based stock predicted model, offers significant improvement over the compared models resulting sharpe ratio of 3.22.

scholarly journals A Hybrid Method for Evaluating of Lightning Performance of Overhead Lines based on Monte Carlo Procedure

2016 ◽  
Vol 67 (4) ◽  
pp. 246-252
Author(s):  
Reza Shariatinasab ◽  
Pooya Tadayon ◽  
Akihiro Ametani
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Risk Analysis ◽  
Hybrid Method ◽  
Overhead Lines ◽  
Monte Carlo Procedure ◽  
Acceptable Accuracy ◽  
The Monte Carlo Method ◽  
Analytical Relations

Abstract This paper proposes a hybrid method for calculating lightning performance of overhead lines caused by direct strokes by combining Lattice diagram together with the Monte Carlo method. In order to go through this, firstly, the proper analytical relations for overvoltages calculation are established based on Lattice diagram. Then, the Monte Carlo procedure is applied to the obtained analytical relations. The aim of the presented method that will be called ‘ML method’ is simply estimation of the lightning performance of the overhead lines and performing the risk analysis of power apparatus with retaining the acceptable accuracy. To confirm the accuracy, the calculated results of the presented ML method are compared with those calculated by the EMTP/ATP simulation.

scholarly journals A Comparative Study of Soft Computing Models for Prediction of Permeability Coefficient of Soil

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Binh Thai Pham ◽  
Manh Duc Nguyen ◽  
Nadhir Al-Ansari ◽  
Quoc Anh Tran ◽  
Lanh Si Ho ◽  
...  
Keyword(s):  
Permeability Coefficient ◽  
Construction Projects ◽  
Statistical Error ◽  
Clay Content ◽  
Cost Effective ◽  
Liquid Limit ◽  
Machine Learning Techniques ◽  
Design Stage ◽  
Accurate Estimation ◽  
Support Vector

Determination of the permeability coefficient (K) of soil is considered as one of the essential steps to assess infiltration, runoff, groundwater, and drainage in the design process of the construction projects. In this study, three cost-effective algorithms, namely, artificial neural network (ANN), support vector machine (SVM), and random forest (RF), which are well-known as advanced machine learning techniques, were used to predict the permeability coefficient (K) of soil (10−9 cm/s), based on a set of simple six input parameters such as natural water content w (%), void ratio (e), specific density (g/cm3), liquid limit (LL) (%), plastic limit (PL) (%), and clay content (%). For this, a total of 84 soil samples data collected from the detailed design stage investigations of Da Nang-Quang Ngai national road project in Vietnam was used to generate training (70%) and testing (30%) datasets for building and validating the models. Statistical error indicators such as RMSE and MAE and correlation coefficient (R) were used to evaluate and compare performance of the models. The results show that all the three models performed well (R > 0.8) for the prediction of permeability coefficient of soil, but the RF model (RMSE = 0.0084, MAE = 0.0049, and R = 0.851) is more efficient compared with the other two models, namely, ANN (RMSE = 0.001, MAE = 0.005, and R = 0.845) and SVM (RMSE = 0.0098, MAE = 0.0064, and R = 0.844). Thus, it can be concluded that the RF model can be used for accurate estimation of the permeability coefficient (K) of the soil.

Benchmarking Risk Analysis Practice in the International Water Sector

2006 ◽  
Vol 1 (2) ◽  
Author(s):  
B.H. MacGillivray ◽  
P.D. Hamilton ◽  
S.E. Hrudey ◽  
L. Reekie ◽  
S.J.T Pollard
Keyword(s):  
Decision Making ◽  
Risk Analysis ◽  
Water Utilities ◽  
Water Sector ◽  
Water Utility ◽  
Analysis Techniques ◽  
The Usa ◽  
International Water ◽  
Practical Guidance ◽  
The Uk

Risk analysis in the water utility sector is fast becoming explicit. Here, we describe application of a capability model to benchmark the risk analysis maturity of a sub-sample of eight water utilities from the USA, the UK and Australia. Our analysis codifies risk analysis practice and offers practical guidance as to how utilities may more effectively employ their portfolio of risk analysis techniques for optimal, credible, and defensible decision making.

scholarly journals Approach to hand posture recognition based on hand shape features for human–robot interaction

2021 ◽  
Author(s):  
Jing Qi ◽  
Kun Xu ◽  
Xilun Ding
Keyword(s):  
Gaussian Mixture ◽  
Human Robot Interaction ◽  
Polar Coordinates ◽  
Support Vector ◽  
Hand Posture ◽  
Data Set ◽  
Hand Shape ◽  
Hand Posture Recognition ◽  
Hand Segmentation ◽  
Posture Recognition

AbstractHand segmentation is the initial step for hand posture recognition. To reduce the effect of variable illumination in hand segmentation step, a new CbCr-I component Gaussian mixture model (GMM) is proposed to detect the skin region. The hand region is selected as a region of interest from the image using the skin detection technique based on the presented CbCr-I component GMM and a new adaptive threshold. A new hand shape distribution feature described in polar coordinates is proposed to extract hand contour features to solve the false recognition problem in some shape-based methods and effectively recognize the hand posture in cases when different hand postures have the same number of outstretched fingers. A multiclass support vector machine classifier is utilized to recognize the hand posture. Experiments were carried out on our data set to verify the feasibility of the proposed method. The results showed the effectiveness of the proposed approach compared with other methods.

scholarly journals Dark spot detection for characterization of marine surface slicks using UAVSAR quad-pol data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Vaishali Chaudhary ◽  
Shashi Kumar
Keyword(s):  
Oil Spills ◽  
Marine Ecosystem ◽  
Dark Spot ◽  
Accurate Estimation ◽  
Support Vector ◽  
Potential Hazard ◽  
Significant Drop ◽  
Oil Slick ◽  
Spot Detection ◽  
Marine Surface

AbstractOil spills are a potential hazard, causing the deaths of millions of aquatic animals and this leaves a calamitous effect on the marine ecosystem. This research focuses on evaluating the potential of polarimetric parameters in discriminating the oil slick from water and also possible thicker/thinner zones within the slick. For this purpose, L-band UAVSAR quad-pol data of the Gulf of Mexico region is exploited. A total number of 19 polarimetric parameters are examined to study their behavior and ability in distinguishing oil slick from water and its own less or more oil accumulated zones. The simulation of compact-pol data from UAVSAR quad-pol data is carried out which has shown good performance in detection and discrimination of oil slick from water. To know the extent of separation between oil and water classes, a statistical separability analysis is carried out. The outcomes of each polarimetric parameter from separability analysis are then quantified with the radial basis function (RBF) supervised Support Vector Machine classifier followed with an accurate estimation of the results. Moreover, a comparison of the achieved and estimated accuracy has shown a significant drop in accuracy values. It has been observed that the highest accuracy is given by LHV compact-pol decomposition and coherency matrix with a classification accuracy of ~ 94.09% and ~ 94.60%, respectively. The proposed methodology has performed well in discriminating the oil slick by utilizing UAVSAR dataset for both quad-pol and compact-pol simulation.

Sign in / Sign up

Export Citation Format

Share Document