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Related Concept Videos

Design Example: Managing Concrete Workability01:14

Design Example: Managing Concrete Workability

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.
To address...
Dynamic Modulus of Elasticity of Concrete01:16

Dynamic Modulus of Elasticity of Concrete

The dynamic modulus of elasticity assesses how a concrete structure deforms under impact or dynamic loads. It is typically higher than the static modulus of elasticity, measured under slow, steady loading conditions.
The sonic test is a common method to determine the dynamic modulus. In this test, a concrete beam, sized either 6 x 6 x 30 inches or 4 x 4 x 20 inches, is clamped at its center. Vibrations are initiated at one end of the beam by an electromagnetic exciter unit powered by a...
Compacting Factor test01:22

Compacting Factor test

The compacting factor test is a method used to assess the workability of concrete. It is  especially suitable for concrete mixes containing aggregates up to one and a half inches in size. This test involves specialized equipment consisting of two truncated cone-shaped hoppers and a cylinder, all with polished interior surfaces to minimize friction.
The procedure begins by placing concrete into the upper hopper without any compaction. Once filled, the bottom door of this hopper is opened,...
Design Consideration01:22

Design Consideration

Designing a structure involves a series of considerations, primarily the material's ultimate strength, calculated through tests that measure changes under increased force until the material reaches its breaking point or limit. The ultimate load, where the material breaks, is divided by its original cross-sectional area, resulting in the ultimate normal stress or strength. The ultimate shearing stress is another significant factor taken into account.
The factor of safety is another key aspect...
Toughness and Hardness of Aggregate01:22

Toughness and Hardness of Aggregate

Toughness and hardness are critical properties of aggregate materials used in concrete, particularly on pavement surfaces and industrial flooring subjected to heavy loads. Toughness is defined as the aggregate's resistance to failure by impact and is measured by the aggregate impact value (AIV). For this, the aggregate impact value test is performed, wherein the impact is delivered by a standard hammer, which falls freely under its own weight onto the aggregates. The aggregates fragment in the...
Design Example: Dimensioning of Concrete Masonry Construction01:13

Design Example: Dimensioning of Concrete Masonry Construction

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 eight feet...

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Related Experiment Video

Updated: Jun 11, 2026

Pore-scale Imaging and Characterization of Hydrocarbon Reservoir Rock Wettability at Subsurface Conditions Using X-ray Microtomography
12:18

Pore-scale Imaging and Characterization of Hydrocarbon Reservoir Rock Wettability at Subsurface Conditions Using X-ray Microtomography

Published on: October 21, 2018

AI and knowledge driven computation of rock mass characteristic parameters across engineering projects.

Zewang Zheng1, Yunpei Zhang2, Quan Xu3

  • 1College of Civil Engineering, Zhejiang University of Technology, Hangzhou, 310014, Zhejiang, China.

Scientific Reports
|June 9, 2026
PubMed
Summary

This study introduces an AI-driven method to predict rock mass quality for tunnel boring machines (TBMs). The approach enhances construction safety and efficiency by accurately identifying rock characteristics using novel parameters and machine learning.

Keywords:
CatBoostCharacteristic parametersCross-project data utilizationEffective rock-breakingRock mass quality predictionTunnel engineering

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Last Updated: Jun 11, 2026

Pore-scale Imaging and Characterization of Hydrocarbon Reservoir Rock Wettability at Subsurface Conditions Using X-ray Microtomography
12:18

Pore-scale Imaging and Characterization of Hydrocarbon Reservoir Rock Wettability at Subsurface Conditions Using X-ray Microtomography

Published on: October 21, 2018

Stress Distribution During Cold Compression of Rocks and Mineral Aggregates Using Synchrotron-based X-Ray Diffraction
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Comprehensive Characterization of Tissue Mineralization in an Ex Vivo Model
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Published on: September 27, 2024

Area of Science:

  • Geotechnical Engineering and Artificial Intelligence
  • Underground Construction and Tunnelling Technology
  • Machine Learning Applications in Civil Engineering

Background:

  • Accurate prediction of rock mass quality ahead of Tunnel Boring Machines (TBMs) is crucial for underground construction efficiency and safety.
  • Existing AI/ML methods face challenges with massive, noisy TBM data, feature selection, and limited data from new projects.
  • Current physics-based and fitting-based methods have limitations in stability, adaptability, and generalizability across diverse geological and engineering conditions.

Purpose of the Study:

  • To propose an AI- and knowledge-driven method for computing reliable rock mass characteristic parameters.
  • To address limitations in existing methods for predicting rock mass quality using TBM data.
  • To develop a robust TBM-based rock mass quality prediction model applicable across multiple projects.

Main Methods:

  • Developed a rock-breaking data filtering method based on effective disc cutter energy conversion.
  • Identified high-efficiency rock-breaking stages using data from three diverse TBM projects (YC, YS, HB).
  • Computed knowledge-driven rock mass characteristic parameters (a, b, Torque Penetration Index - TPI) as input features.
  • Employed a CatBoost AI model for rock mass class prediction, tested across different projects.

Main Results:

  • The proposed characteristic parameters demonstrated a strong correlation with actual rock mass quality.
  • Achieved high prediction accuracies: 85.60% (YC), 86.48% (YS), and 88.89% (HB), outperforming conventional methods.
  • The AI-driven approach showed superior performance and robustness in multi-project scenarios.

Conclusions:

  • The AI- and knowledge-driven method effectively computes rock mass characteristic parameters for improved prediction accuracy.
  • This approach offers new technical support for cross-project data utilization and real-time rock mass quality prediction.
  • The findings have significant implications for enhancing construction safety and efficiency in new tunnel projects.