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関連する概念動画

Workability of Concrete01:25

Workability of Concrete

157
The workability of concrete is a crucial property that affects its handling, placing, and finishing during construction. It describes the ease with which concrete can be mixed, placed, compacted, and finished. Workability is primarily concerned with the concrete's movement and its ability to resist internal friction and external resistance from molds and reinforcements during the application process.
Concrete's workability is determined by its resistance to internal forces that arise...
157
Pozzolans01:21

Pozzolans

190
Pozzolans are siliceous or aluminous materials blended with Portland cement. They interact with the calcium hydroxide produced during the hydration of Portland cement and contribute to improved strength and durability of concrete. The pozzolanic activity, a measure of a pozzolan's effectiveness, is typically assessed using the strength activity index, as defined in ASTM C 618-93, which calculates the ratio of the compressive strength of cement mixtures with and without pozzolan.
Fly ash is...
190
Design Example: Managing Concrete Workability01:14

Design Example: Managing Concrete Workability

121
This example deals with managing the workability of concrete for a raft foundation project under hot weather conditions. Workability is crucial for ensuring the concrete is easy to place, compact, and finish. In this scenario, a slump test — a common method to measure the workability of fresh concrete — initially indicated low workability. This was attributed to the rapid water loss from the concrete mix, exacerbated by the high temperatures causing the course aggregates to heat up.
121
Effects of Air-entrainment in Concrete01:28

Effects of Air-entrainment in Concrete

140
Air entrainment in concrete significantly enhances the material's durability, especially in environments subjected to freeze-thaw cycles. Introducing small air bubbles into the concrete mix acts as internal voids that accommodate the expansion of water when it freezes, thereby alleviating internal stress and preventing structural cracks. This function is crucial in climates with significant freezing and thawing, as it protects the concrete from repeated stresses that could lead to premature...
140
Fatigue Strength of Concrete01:22

Fatigue Strength of Concrete

285
Fatigue, in the context of materials science and engineering, refers to the weakening or failure of a material caused by repeatedly applied loads, even if these loads are below the strength limit of the material. Fatigue strength in concrete is a critical property that influences its durability and longevity. Concrete can fail in two ways due to fatigue. Static fatigue or creep rupture occurs under a constant load or one that increases slowly. The other failure mode is due to cyclical or...
285
Design Example: Sustainability in Concrete Building01:26

Design Example: Sustainability in Concrete Building

226
As the construction industry moves towards more eco-friendly practices, concrete's adaptability and its ability to incorporate sustainable features make it a key material in the drive towards greener building solutions.
There are multiple approaches to achieve sustainability in a commercial concrete building. For instance, construct a concrete parking area under the building, utilizing pervious concrete paver blocks in open areas to facilitate rainwater collection through an underground...
226

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関連する実験動画

Updated: Sep 10, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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フライアッシュベースの持続可能なコンクリートの最適化と予測性能は,解釈可能な機械学習技術による統合されたマルチタスクのディープラーニングフレームワークを使用します.

Bhupesh P Nandurkar1, Jayant M Raut1, Pawan K Hinge1

  • 1Department of Civil Engineering, Yeshwantrao Chavan College of Engineering, Nagpur, 441110, Maharashtra, India.

Scientific reports
|August 21, 2025
PubMed
まとめ

この研究では,フライアッシュを含むコンクリートの強さの正確な予測のためのハイブリッドAIモデルを導入しています. この解釈可能なモデルは,強度因子に関する明確な洞察を提供することで,建設の安全性と材料設計を向上させます.

キーワード:
自動ML最適化についてコンクリートの圧縮強度ディープニューラルネットワークフライアッシュの反応性グラデーションの強化マルチタスク学習の枠組み非破壊的な試験SHAPとLIMEの解釈性について

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科学分野:

  • 材料科学と工学
  • 土木工学
  • 建築における人工知能

背景:

  • コンクリートの強さの正確な予測は,建設の安全性と品質保証に不可欠です.
  • 既存の方法は,特にフライアッシュのような補足的なセメント材料によって,正確性と解釈性の間でしばしば妥協します.
  • 複雑なデザインと環境要因に対応できる 解釈可能なモデルが不可欠です

研究 の 目的:

  • コンクリートの圧縮力と張力強さを予測するための非常に正確で解釈可能なハイブリッドモデルを開発する.
  • マルチタスク・ラーニング (MTL) フレームワーク内でミックス・デザインの変数,環境要因,非破壊的テスト (NDT) のデータを統合する.
  • 先進的な機械学習技術を活用し,予測の正確性とモデル説明性を向上させる.

主な方法:

  • グラデーションブースト (XGBoost) とディープニューラルネットワーク (DNN) を組み合わせたハイブリッドアプローチが採用されました.
  • AutoGluonは,マルチタスクラーニング (MTL) フレームワーク内の自動モデルの最適化に使用されました.
  • SHAP (SHapley Additive exPlanations) とLIME (Local Interpretable Model-agnostic Explanations) を使って,グローバルとローカルの解釈が可能になった.

主要な成果:

  • このモデルは,テストセットで0.91の印象的なR2スコアを達成しました.
  • 平均二乗誤差 (MSE) が23%減少し,既存のモデルを上回りました.
  • 特徴分析では,フライアッシュの割合が予測に大きく影響し,約25%を占めていることが示された.

結論:

  • 提案されたハイブリッドモデルは,解釈可能なコンクリートの強さの予測のための堅固なプラットフォームを提供します.
  • この発見は,ハイブリッドモデリングの橋渡し,自動化された最適化,そしてコンクリートアプリケーションの説明性における重要な進歩を示しています.
  • この研究は,材料設計を最適化し,建設における構造的整合性を確保するための大きな希望を持っています.