constructionproductivity

Construction productivity prediction through Bayesian networks for building projects: case from Vietnam.

A collection of Bayesian networks (discrete, Gaussian, and conditional linear Gaussian) collated from recent academic literature. The 'bnRep_summary' object provides an overview of the Bayesian networks in the repository and the package documentation includes details about the variables in each network. A Shiny app to explore the repository can be launched with 'bnRep_app()' and is available online at <https://manueleleonelli.shinyapps.io/bnRep>. Reference: 'M. Leonelli' (2025) <doi:10.1016/j.neucom.2025.129502>.

Manuele Leonelli

bnRep

A Repository of Bayesian Networks from the Academic Literature

constructionproductivity function

A discrete Bayesian network to identify causal relationship and occurrence probability of critical factors affecting construction productivity. Probabilities were given within the referenced paper. The vertices are:<dl>
<dt>Accidents</dt>
<dd>(Yes, No);</dd><dt>AdverseWeather</dt>
<dd>(Yes, No);</dd><dt>Age</dt>
<dd>(Yes, No);</dd><dt>Attitude</dt>
<dd>(Yes, No);</dd><dt>EngineerQualification</dt>
<dd>(Yes, No);</dd><dt>Experience</dt>
<dd>(Yes, No);</dd><dt>HealthStatus</dt>
<dd>(Yes, No);</dd><dt>MaterialPresence</dt>
<dd>(Yes, No);</dd><dt>OwnerFinance</dt>
<dd>(Yes, No);</dd><dt>PlanningAndMethod</dt>
<dd>(Yes, No);</dd><dt>Productivity</dt>
<dd>(Yes, No);</dd><dt>Sex</dt>
<dd>(Yes, No);</dd><dt>SkilledWorkers</dt>
<dd>(Yes, No);</dd><dt>TaskComplexity</dt>
<dd>(Yes, No);</dd><dt>TechnologyLevel</dt>
<dd>(Yes, No);</dd><dt>WorkingFrequency</dt>
<dd>(Yes, No);</dd><dt>WorkingTools</dt>
<dd>(Yes, No);</dd><dt>Workmanship</dt>
<dd>(Yes, No);</dd>
</dl><div class="sourceCode"></div><pre><code>@return An object of class \code{bn.fit}. Refer to the documentation of \code{bnlearn} for details.
</code></pre>

Format

constructionproductivity Bayesian Network — constructionproductivity

A discrete Bayesian network to identify causal relationship and occurrence probability of critical factors affecting construction productivity. Probabilities were given within the referenced paper. The vertices are:<dl>
<dt>Accidents</dt>
<dd>(Yes, No);</dd>

<dt>AdverseWeather</dt>
<dd>(Yes, No);</dd>

<dt>Age</dt>
<dd>(Yes, No);</dd>

<dt>Attitude</dt>
<dd>(Yes, No);</dd>

<dt>EngineerQualification</dt>
<dd>(Yes, No);</dd>

<dt>Experience</dt>
<dd>(Yes, No);</dd>

<dt>HealthStatus</dt>
<dd>(Yes, No);</dd>

<dt>MaterialPresence</dt>
<dd>(Yes, No);</dd>

<dt>OwnerFinance</dt>
<dd>(Yes, No);</dd>

<dt>PlanningAndMethod</dt>
<dd>(Yes, No);</dd>

<dt>Productivity</dt>
<dd>(Yes, No);</dd>

<dt>Sex</dt>
<dd>(Yes, No);</dd>

<dt>SkilledWorkers</dt>
<dd>(Yes, No);</dd>

<dt>TaskComplexity</dt>
<dd>(Yes, No);</dd>

<dt>TechnologyLevel</dt>
<dd>(Yes, No);</dd>

<dt>WorkingFrequency</dt>
<dd>(Yes, No);</dd>

<dt>WorkingTools</dt>
<dd>(Yes, No);</dd>

<dt>Workmanship</dt>
<dd>(Yes, No);</dd>


</dl>

<div class="sourceCode"><pre><code>@return An object of class \code{bn.fit}. Refer to the documentation of \code{bnlearn} for details.
</code></pre></div>

constructionproductivity: constructionproductivity Bayesian Network

Description

Arguments

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References