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Related Concept Videos

Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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Classification of Systems-I01:26

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Related Experiment Video

Updated: Dec 14, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

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Risk management system and intelligent decision-making for prefabricated building project under deep learning

Huazan Liu1, Yukang He1, Qichao Hu1

  • 1School of Civil Engineering and Architecture, Nanchang University, Nanchang, PR China.

Plos One
|July 18, 2020
PubMed
Summary

This study introduces an intelligent risk management system for prefabricated buildings using a Modified Teaching-Learning-Based-Optimization (MTLBO) and Backpropagation (BP) neural network. This advanced model enhances prediction accuracy for construction project risks.

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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

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Area of Science:

  • Construction Management
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Prefabricated construction projects face complex risks requiring robust management systems.
  • Existing risk management models may lack the predictive accuracy needed for large-scale projects.

Purpose of the Study:

  • To develop an intelligent risk management system for prefabricated building projects.
  • To improve the accuracy and efficiency of risk prediction in construction.

Main Methods:

  • Implemented a deep learning multilayer feedforward neural network (Backpropagation, BP).
  • Modified the Teaching-Learning-Based Optimization (TLBO) algorithm using information entropy (MTLBO).
  • Established and tested an MTLBO-BP neural network prediction model.

Main Results:

  • The MTLBO algorithm demonstrated superior global searchability compared to TLBO, avoiding local optima.
  • The MTLBO-BP model showed faster convergence and better prediction performance.
  • The intelligent risk management system achieved higher accuracy in reliability and cost prediction.

Conclusions:

  • The proposed MTLBO-BP algorithm provides a robust framework for intelligent risk management in prefabricated construction.
  • This approach offers theoretical support for enhanced decision-making and quality assurance in construction projects.
  • The algorithm is essential for effective construction project management and risk mitigation.