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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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Updated: Sep 10, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

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Optimización y rendimiento predictivo del hormigón sostenible basado en cenizas volantes utilizando un marco de

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
Resumen

Este estudio introduce un modelo híbrido de IA para una predicción precisa de la resistencia del concreto, incorporando cenizas volantes. El modelo interpretable mejora la seguridad de la construcción y el diseño de materiales al proporcionar información clara sobre los factores de resistencia.

Palabras clave:
Optimización de AutoMLResistencia a la compresión del hormigónRedes neuronales profundasReactividad de las cenizas volantesMejora del gradienteMarco de aprendizaje multitareaPruebas no destructivasInterpretabilidad de SHAP y LIME

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Área de la Ciencia:

  • Ciencias de los materiales e ingeniería
  • Ingeniería civil
  • Inteligencia artificial en la construcción

Sus antecedentes:

  • La predicción precisa de la resistencia del concreto es crucial para la seguridad de la construcción y la garantía de calidad.
  • Los métodos existentes a menudo hacen concesiones entre la precisión y la interpretabilidad, particularmente con materiales cementíferos suplementarios como la ceniza volante.
  • La necesidad de modelos interpretables que puedan manejar diseños de mezcla complejos y factores ambientales es primordial.

Objetivo del estudio:

  • Desarrollar un modelo híbrido altamente preciso e interpretable para predecir la resistencia a la compresión y a la tracción del concreto.
  • Integrar las variables de diseño de la mezcla, los factores ambientales y los datos de pruebas no destructivas (NDT) dentro de un marco de aprendizaje multitarea (MTL).
  • Aprovechar técnicas avanzadas de aprendizaje automático para mejorar la precisión de la predicción y la explicabilidad del modelo.

Principales métodos:

  • Se empleó un enfoque híbrido que combina el aumento del gradiente (XGBoost) y las redes neuronales profundas (DNNs).
  • AutoGluon se utilizó para la optimización automatizada de modelos dentro de un marco de aprendizaje multitarea (MTL).
  • La explicabilidad se logró utilizando SHAP (Shapley Additive Explanations) y LIME (Local Interpretable Model-agnostic Explanations) para la interpretación global y local.

Principales resultados:

  • El modelo logró una puntuación R2 impresionante de 0,91 en el conjunto de pruebas.
  • Se observó una reducción del 23% en el error cuadrado medio (MSE), superando los modelos existentes.
  • El análisis de las características indicó que el porcentaje de cenizas volantes influye significativamente en las predicciones, contribuyendo aproximadamente en un 25%.

Conclusiones:

  • El modelo híbrido propuesto ofrece una plataforma robusta para la predicción de la resistencia del concreto interpretable.
  • Los hallazgos demuestran un avance significativo en el modelado híbrido, la optimización automatizada y la explicabilidad para aplicaciones concretas.
  • Este trabajo es muy prometedor para optimizar el diseño de materiales y garantizar la integridad estructural en la construcción.