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01:12Modeling and Similitude
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Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
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01:05Estimation of the Physical Quantities
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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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01:30Typical Model Studies
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Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
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01:24Design Example: Aggregate Gradation
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The right type and quality of aggregates are crucial for concrete as they significantly influence its properties, mix proportions, and cost-effectiveness. If different sources are available for sand, the commonly used fine aggregate in concrete, the selection of sand is primarily based on its gradation.
The grading, or particle-size distribution, of sand is determined using sieve analysis, with standard sizes ranging from 150 μm to 10 mm (ASTM No. 100 sieve to 3⁄8 in. sieve). Sand is...
The grading, or particle-size distribution, of sand is determined using sieve analysis, with standard sizes ranging from 150 μm to 10 mm (ASTM No. 100 sieve to 3⁄8 in. sieve). Sand is...
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01:03Prediction 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.
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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01:19Expected Frequencies in Goodness-of-Fit Tests
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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
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Research on engineering cost prediction based on similarity.
1School of Architecture and Engineering, Fu Zhou Polytechnic, Fuzhou, 350108, Fujian, China.
Scientific Reports
|July 2, 2025
Summary
This study introduces a Vague Similarity (VS) method for accurate construction cost prediction, overcoming limitations in rapid quotation processes. The approach enhances prediction accuracy and reduces computational complexity for engineering projects.
Area of Science:
- Construction Management
- Engineering Economics
- Data Science
Background:
- Rapid quotation processes in construction projects often suffer from inaccurate cost predictions and high computational complexity.
- Uncertainty in unit prices, influenced by factors like season, location, and market conditions, is a key challenge, presenting as quotation value ranges.
Purpose of the Study:
- To propose a novel method for construction cost prediction that addresses inaccuracies and computational complexity in rapid quotation.
- To leverage Vague Similarity (VS) to handle the inherent uncertainty in construction project unit prices.
Main Methods:
- A conversion technique transforms interval values (representing uncertain unit prices) into Vague values.
- The engineering characteristic matrix is converted into a Vague matrix for analysis.
- Vague Similarity (VS) is employed to identify reference projects closely matching the target project's characteristics.
Main Results:
- The method successfully identifies similar reference projects based on Vague Similarity.
- Unit costs for the intended construction project are predicted using formulas applied to the identified similar projects.
- A case study demonstrates the practical application and validates the method's rationality.
Conclusions:
- The proposed Vague Similarity (VS) method offers a rational and effective approach to construction cost prediction.
- This method enhances accuracy and reduces computational complexity compared to traditional rapid quotation techniques.
- The transformation of interval data into Vague values is crucial for handling price uncertainties in construction.