scholarly journals Analysis of modern construction projects using montecarlo simulation technique

2018 ◽  
Vol 7 (2.19) ◽  
pp. 41
Author(s):  
A. M. Arunmohan ◽  
M. Lakshmi
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Quantitative Analysis ◽  
Construction Projects ◽  
Project Success ◽  
Risk Identification ◽  
Project Schedule ◽  
Montecarlo Simulation ◽  
Total Project Cost ◽  
Total Project

Today, Construction based Industry is the prospering industry which has a high economical influence on any nation. Delay in the huge construction project increases the total project cost. Henceforth, uncertainties as well as risks must be significantly regarded during the project. For organizing and completing the projects in a financially, timely and qualitatively accountable manner, careful scheduling of projects is compulsory. Effectual scheduling of project assures project success. This study concentrates on qualitative analysis, risk identification, together with quantitative analysis. The targets are i) to ascertain the key risk aspects that disturb the project schedule, and ii) to find the probability of finishing the project within specified time. Questionnaires are distributed amongst 20 industry practitioners with disparate experience from [1] to [25] years.  Quantitative analysis is made by the methods like Monte Carlo simulation (MCS) and PERT. @RISK by Palisade corp. is utilized for MCS.

On Using Monte Carlo Simulations for Project Risk Management

2016 ◽  
pp. 150-173
Author(s):  
Cristiana Tudor ◽  
Maria Tudor
Keyword(s):  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Decision Makers ◽  
Project Schedule ◽  
Project Risk ◽  
Monte Carlo Simulation Technique ◽  
Basic Probability ◽  
The Cost

This chapter covers the essentials of using the Monte Carlo Simulation technique (MSC) for project schedule and cost risk analysis. It offers a description of the steps involved in performing a Monte Carlo simulation and provides the basic probability and statistical concepts that MSC is based on. Further, a simple practical spreadsheet example goes through the steps presented before to show how MCS can be used in practice to assess the cost and duration risk of a project and ultimately to enable decision makers to improve the quality of their judgments.

Investigation of the particle size-dependent near-infrared spectral features of binary mixture samples in conjunction with Monte Carlo simulation and the influence of particle size on the accuracy of quantitative analysis

The Analyst ◽  
2018 ◽  
Vol 143 (18) ◽  
pp. 4306-4315 ◽  
Author(s):  
Pham K. Duy ◽  
Seulah Chun ◽  
Yoonjeong Lee ◽  
Hoeil Chung
Keyword(s):  
Monte Carlo Simulation ◽  
Particle Size ◽  
Monte Carlo ◽  
Quantitative Analysis ◽  
Near Infrared ◽  
Spectral Variation ◽  
Spectral Features ◽  
Size Dependent ◽  
Infrared Spectral ◽  
Mixture Samples

The origin of particle size-induced near-infrared (NIR) spectral variation, which is fundamental for robust quantitative analysis, was systematically studied in conjunction with Monte Carlo simulation.

Research on Method about Determination of Condition-Based Maintenance Objects by Quantitative Analysis

2014 ◽  
Vol 635-637 ◽  
pp. 2023-2028
Author(s):  
Xiang Zan ◽  
Shi Xin Zhang ◽  
Yi Zheng ◽  
Yan Chao Liu
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Regression Analysis ◽  
Quantitative Analysis ◽  
Essential Role ◽  
Condition Based Maintenance ◽  
Traditional Methods

As an essential role in determination of condition-based maintenance (CBM) objects ,necessity and applicability analysis are both important. Necessity analysis is first and applicability analysis is second is proposed. Due to shortcoming of traditional methods, a quantitative is proposed. The key of method are criticality evaluation based on Monte Carlo simulation and applicability analysis based on regression analysis, which can solve the problem short of unite standard and influence by subjective factors. The result shows the model works well.

scholarly journals Dynamic Cooperative Model for Ranking Construction Risks using Monte Carlo Simulation

2020 ◽  
Vol 9 (12) ◽  
pp. 211-216
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Construction Projects ◽  
Critical Path ◽  
Quantitative Model ◽  
Decision Makers ◽  
Path Model ◽  
Simulation Engine ◽  
Construction Risks ◽  
Risk Planning

Construction projects suffer from diverse uncertainties that hinder the key objectives’ achievement. These uncertainties represent risks that may appear through the project life cycle. This paper introduces a quantitative model to estimate and rank risks dynamically during the risk planning phase. Such ranking would help decision-makers appropriately respond to and/or control construction risks. The model provides proper risk contingency reserves for both project time and cost that meet decision-makers' selected confidence levels using Monte Carlo Simulation (MCS). In order to quantify the project uncertainty, severities of residual risks are determined and allocated at the project's activities-level using a planning/scheduling spreadsheet model and a MCS tool suitable for spreadsheets. The model is able to calculate the contribution of each risk from the determined contingency at both the project level for both the time and cost at the decision-maker confidence level.The model represents a direct implementation for a Risk Planning Contingency Model (RPCM); which involves four modules as follows: (1) Risk Register (RR), (2) Risk Allocator (RA), (3) Risk Simulator (RS), and (4) Contingency Calculator (CC). These modules are hosted in a critical path model scheduling spreadsheet to facilitate risk management. In addition, a simulation engine add-in is used for analyzing the probability distribution for the project time and cost outcomes. In order to verify the proposed model, the process and analysis have been applied to a case study project. The results show that the RPCM is capable to rank and estimate the residual risks in an easy, fast, and effective way.

scholarly journals The Analysis of Cost Estimation using Cost Significant Model on Bridge Construction in South Sulawesi

2021 ◽  
Vol 921 (1) ◽  
pp. 012073
Author(s):  
E Aprianti ◽  
S Hamzah ◽  
M A Abdurrahman
Keyword(s):  
Regression Analysis ◽  
Cost Estimation ◽  
Construction Projects ◽  
Steel Frame ◽  
Bridge Construction ◽  
South Sulawesi ◽  
The Cost ◽  
Total Project Cost ◽  
Total Project ◽  
Significant Model

Abstract One of the fundamental problems faced by the province of South Sulawesi is the factor of accessibility, so the role of bridges is quite important. For this reason, the budget planning for standard bridge construction projects also needs to be efficient in terms of preparation and accurate in terms of budget. The Cost Significant Model is one of the total construction cost estimation models that relies more on the prices that have the most influence on the total project cost as the basis for estimation. In general, this study uses data from steel frame bridge construction projects in South Sulawesi Province to formulate a mathematical model with linear regression analysis so that it can be used in the process of estimating similar projects going forward. The Estimation Model which is formed from the regression analysis and the Cost Significant Model in this study, namely; Y = 3.884 (X7) + 0.989 (X8) - 65515.372. With; Y = Estimated Total Cost (Rp/m); X7 = Reinforcement Work Cost (Rp/m); X8 = Steel Frame Structure Work Cost (Rp/m). Where this model can explain 99.7% of the total project cost with a cost model factor of 1.038. The level of accuracy (percentage error estimate) of the estimation results of the Cost Significant Model in this study ranges from - 1.46% to +2.45%.

scholarly journals The utilization of project risk monitoring and control practices and their relationship with project success in construction projects

2022 ◽  
Vol 7 (1) ◽  
pp. 35-52 ◽  
Author(s):  
Kennedy Christopher Obondi
Keyword(s):  
United States ◽  
Construction Projects ◽  
Project Success ◽  
The United States ◽  
Risk Identification ◽  
Project Managers ◽  
Monitoring And Control ◽  
Project Risk ◽  
Risk Monitoring ◽  
And Control

Risk monitoring and control is often poorly implemented in construction projects because of a failure to monitor and manage identified risks. Construction companies experience significant losses due to project managers' lack of project risk monitoring and control in construction projects. Most studies have concentrated on risk identification, risk assessment, and risk analysis processes while neglecting crucial risk management processes of risk control, risk monitoring, and risk response. The lack of research on these three crucial processes highlights a gap in the literature concerning how these processes can increase the delivery of successful projects. The purpose of this study was to examine whether the utilization of project risk monitoring and control practices was related to project success in construction projects in the United States. An electronic survey instrument was used to collect data from a sample of 50 construction project managers in the Dallas-Fort Worth area in the state of Texas, in the United States. Spearman rho correlation analysis was used to examine the relationship between project risk monitoring and control practices and project success. The results of this study indicated that all project risk monitoring and control practices, including risk reassessment, risk audits, contingency reserves analysis, and risk status meetings, were significantly and positively related to project success in construction projects. One of the recommendations presented in this study was that future research should conduct the same study in developing countries to see if the study’s findings remain the same and generalizable. The study concluded that construction organizations should regularly consider the importance and usage of project risk monitoring and control practices and apply them to improve the success rate of a project.

scholarly journals Beta Distribution for Estimating Risk Associated Time Schedule of Hydropower Projects in Nepal

2021 ◽  
pp. 67-78
Author(s):  
Kiran Kumar Shrestha ◽  
Rabindra Kayastha
Keyword(s):  
Quantitative Analysis ◽  
Construction Projects ◽  
Construction Project ◽  
Simulation Method ◽  
Development Project ◽  
Project Work ◽  
Time Schedule ◽  
Project Schedule ◽  
Expected Time ◽  
Hydropower Construction

Background: Risk is associated with every kind of project work whether it is related to engineering construction project, software development project, financial transaction process or business process. There isn't any project which is free of risks. It is inherent in all types of projects. Observing risk associated with a project can help in successful completion of projects in expected time and expected cost with good assurance of quality. This article is concerned with quantitative analysis of risks coined with hydropower construction project in Nepal. Objective: The main objective of this paper is (a) to identify different activities involved in hydropower construction projects (b) to estimate risk associated time schedule of the identified project activities. Materials and Methods: Data required for the fulfillment of the objective are obtained by interview and discussion with executives of “Shiva Shree Hydropower Limited” and by using project schedule charts of projects won by the company. In this article quantitative analysis of schedule risk of hydropower project is studied by simulation method. Results: Different activities involved in hydropower construction project are identified. Also, risk associated with time schedule of project are observed quantitatively by simulation using beta-PERT distribution. Conclusion: Estimation of time schedule associated with project activities is more realistic when it is analyzed by using beta-PERT distribution compared to other statistical distributions.

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