Prediction Model Safety Perfomance Model on The Dam Construction Project Based Bayesian Networks

ABSTRACT The Calaveras Dam Replacement Project, a major construction project completed in 2019, involved hundreds of workers using heavy earth-moving equipment and mining operations, including blasting, drilling, rock crushing, and other operations designed to move millions of cubic yards of earth. Much of the material was composed of serpentinite, blueschist, and other rocks that contain chrysotile and a variety of amphibole minerals, including glaucophane, winchite, actinolite, tremolite, and other asbestos-related amphiboles. This article explores the unique characteristics of the blueschist that required extensive protective measures to be undertaken by the contractor to protect workers and surrounding sensitive receptors. This article will provide an overall summary of the dimensional characteristics of the airborne blueschist elongate mineral particles encountered during construction activities to compare and contrast current understanding of cleavage fragments versus asbestiform mineral fibers.

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A Prediction Model for Construction Project Time Contingency

Construction Research Congress 2009 ◽

10.1061/41020(339)75 ◽

2009 ◽

Cited By ~ 3

Author(s):

Dalia Mohamed ◽

Florida Srour ◽

Wael Tabra ◽

Tarek Zayed

Keyword(s):

Prediction Model ◽

Construction Project

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Creation of a roost environment considering the habitat characteristics of Little japanese horseshoe bat (Rhinolophus cornutus Temminck, 1835) in Koishiwaragawa Dam Construction Project

Ecology and Civil Engineering ◽

10.3825/ece.21.61 ◽

2018 ◽

Vol 21 (1) ◽

pp. 61-68

Author(s):

Hiroshi MURATA ◽

Shusei ARAI ◽

Haruo NAKANO

Keyword(s):

Construction Project ◽

Habitat Characteristics ◽

Dam Construction ◽

Horseshoe Bat

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Implementation of lean construction and critical chain project management (CCPM) for waste management and work estimation on the Ciawi dam construction project

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/794/1/012074 ◽

2021 ◽

Vol 794 (1) ◽

pp. 012074

Author(s):

Fransisca Dini Ariyanti ◽

Annatania Calista Putri ◽

Diah Ayu Ningtyas

Keyword(s):

Project Management ◽

Waste Management ◽

Construction Project ◽

Lean Construction ◽

Dam Construction ◽

Critical Chain Project Management ◽

Critical Chain

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An Education Driven Model for Non-Communicable Diseases Care

Advances in Medical Education, Research, and Ethics - Handbook of Research on Advancing Health Education through Technology ◽

10.4018/978-1-4666-9494-1.ch017 ◽

2016 ◽

pp. 391-418 ◽

Cited By ~ 1

Author(s):

Fábio Pittoli ◽

Henrique Damasceno Vianna ◽

Jorge Luis Victória Barbosa

Keyword(s):

Risk Factors ◽

Prediction Model ◽

Bayesian Networks ◽

Chronic Diseases ◽

Communicable Disease ◽

Communicable Diseases ◽

Treatment Recommendations ◽

Non Communicable Diseases ◽

Cause And Effect ◽

Non Communicable Disease

Patients with chronic diseases should be made aware of their planned treatments as well as being kept informed of the progress of those treatments. The Chronic Prediction model was designed not only to educate patients and assist them with some chronic non-communicable disease, but to control the risk factors that affect their diseases. The model utilizes Bayesian networks to map three things: to identify the cause and effect relationships among existing risk factors; to provide treatment recommendations about these risk factors and; to aid caregivers in the treatment of the patients.

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Schedule risk analysis in construction project using RFMEA and Bayesian networks: The Cali-Colombia case study

2013 IEEE International Conference on Industrial Engineering and Engineering Management ◽

10.1109/ieem.2013.6962445 ◽

2013 ◽

Author(s):

C. A. Mican ◽

V. Jimenez ◽

J. Perez ◽

J. A. Borrero

Keyword(s):

Risk Analysis ◽

Bayesian Networks ◽

Construction Project ◽

Schedule Risk ◽

Schedule Risk Analysis

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Research of Risk Control of Public Construction Project Based on Fault Tree

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.457-458.1682 ◽

2013 ◽

Vol 457-458 ◽

pp. 1682-1685

Author(s):

Liu Chang ◽

Fu Zhou Luo

Keyword(s):

Construction Project ◽

Fault Tree ◽

Fault Tree Analysis ◽

Dam Construction ◽

Layer By Layer ◽

Analysis Method ◽

Project Schedule ◽

Tree Model ◽

Project Characteristics ◽

Public Construction

According to characteristics of the project characteristics, fault tree analysis method is adopted and fault tree model is established to decompose the reasons that affect the project schedule layer by layer. Taking a dam construction project as an example, according to the degree of structural order, various factors are explored to calculate the degree of influence of the delay in progress, and the corresponding measures are proposed to control the schedule.

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Multi-Step Prediction Algorithm for State Prediction Model

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.143-144.634 ◽

2010 ◽

Vol 143-144 ◽

pp. 634-638

Author(s):

Zi Li Zhang ◽

Hong Wei Song

Keyword(s):

Prediction Model ◽

Bayesian Networks ◽

Influencing Factors ◽

Dynamic Bayesian Networks ◽

Prediction Algorithm ◽

Time Varying ◽

System State ◽

Reasoning Process ◽

State Prediction ◽

The Relationship

Dynamic Bayesian networks can be well dealt with the time-varying multivariable problem. The state model based on Dynamic Bayesian networks can more accurately describe the relationship between the system state and the influencing factors. In this paper, the width of the reasoning is used to simplify the amount of data in the reasoning process. Multi-step state prediction is achieved by extending time-slice. Experiment has shown that the proposed algorithm can achieve better prediction results.

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