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Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Design Example: Dimensioning of Concrete Masonry Construction01:13

Design Example: Dimensioning of Concrete Masonry Construction

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For the construction of a storeroom using concrete masonry units, it's essential to align the dimensions of the structure with the actual sizes of the blocks and the intended mortar joints. On the site in question, there's a stockpile of concrete masonry blocks with a nominal size of eight by eight by sixteen inches, which are to be used in the construction of the storeroom.
The site engineer has laid out a plan for the storeroom with external dimensions of twelve feet in length and...
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Method of Superposition01:20

Method of Superposition

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The method of superposition is a crucial technique in structural engineering, used to analyze the effect of multiple loads on beams. This approach involves calculating the deflection and slope for each load on a beam separately, and then summing these effects to determine the overall impact. It is applicable only when the beam material remains within its elastic limit, ensuring that deformations are linearly elastic.
When applying the method of superposition, each type of load—whether...
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Multiple Regression01:25

Multiple Regression

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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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DSCostPred: a double-stacking model for construction cost prediction.

Chen-Ping Liu1, Xin-Gen Sun2, Jian-Hua Guan3

  • 1Department of Architectural Engineering, Hunan Defense Industry Polytechnic, Xiangtan, 411207, China.

Scientific Reports
|December 20, 2025
PubMed
Summary

Accurate construction cost prediction is challenging due to complex variable interactions. A novel dual-stacking method (DSCostPred) improves prediction by classifying variables and using ensemble models, outperforming traditional approaches.

Keywords:
Construction cost predictionDual-stacking methodModel stackingVariable stacking

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Area of Science:

  • Engineering
  • Construction Management
  • Data Science

Background:

  • Construction cost prediction is crucial but complicated by multi-dimensional, dynamic variables.
  • Nonlinear relationships and interactions among variables reduce prediction accuracy.

Purpose of the Study:

  • To develop an advanced method for accurate construction project cost prediction.
  • To address the challenges posed by complex variable interactions and functional differences.

Main Methods:

  • Proposed a dual-stacking construction cost prediction method (DSCostPred).
  • Implemented variable stacking for pre-classification to prevent interference.
  • Utilized model stacking with diverse algorithms to capture complex interactions.
  • Integrated variable and model stacking for collaborative predictions.

Main Results:

  • DSCostPred demonstrated superior performance compared to classical methods on real-world data.
  • Ablation experiments confirmed the effectiveness of the dual-stacking approach.
  • SHAP analysis validated the feasibility and interpretability of the proposed method.

Conclusions:

  • The dual-stacking approach effectively handles complex variable relationships in construction cost prediction.
  • DSCostPred offers a robust and accurate solution for engineering construction projects.
  • Variable classification and ensemble modeling are key to improving prediction accuracy.