Concrete Quality Control Using Sensors for Real-Time Slump Analysis
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Solution Overview
Problem
The construction industry faces challenges in efficiently and cost-effectively controlling the quality of construction materials, particularly in determining structural properties such as strength and shrinkage, to meet tighter deadlines and reduce liabilities.
Innovation Solution
Implementing a system that utilizes embedded sensors and machine learning/ai to monitor and analyze construction materials throughout their life cycle, including torque, hydraulic pressure, angular velocity, and temperature, to determine yield stress, slump, and other properties, optimizing mix designs and ensuring compliance with specifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional quality control methods are used for construction materials, then cost reduction and efficiency improvement are limited, but implementing advanced sensor systems and machine learning increases device complexity and initial costs
Solution Approach 1:
The drum is equipped with multiple sensors (accelerometer, gyroscope, hydraulic pressure sensors, temperature sensors) that serve multiple functions: monitoring drum rotation, measuring concrete viscosity, tracking hydraulic system performance, and detecting temperature variations. This multi-functional approach consolidates several measurement capabilities into a single integrated system, improving construction efficiency without proportionally increasing system complexity
Solution Approach 2:
The system automatically processes sensor data through machine learning algorithms to determine yield stress, slump, and other concrete properties without requiring manual laboratory testing. The automated analysis and real-time feedback enable the system to self-regulate and optimize concrete quality control, reducing the need for external quality control personnel and procedures
2Measurement precision
If manual quality control testing is performed, then measurement precision is limited, but real-time sensor monitoring provides continuous data at increased device complexity
Solution Approach 1:
Traditional manual quality control methods involving physical sampling and laboratory testing are replaced with an automated sensor-based monitoring system. Accelerometers, gyroscopes, and hydraulic pressure sensors continuously measure concrete properties in real-time, providing precise data on yield stress, viscosity, and slump without requiring manual intervention or external laboratory facilities
Solution Approach 2:
The system continuously collects data from multiple sensors and provides real-time feedback on concrete quality parameters. Machine learning algorithms analyze the sensor data and provide immediate feedback on yield stress, slump, and compliance with specifications, enabling及时调整 of mixing or placement operations to maintain optimal concrete quality
3Loss of information
If comprehensive sensor monitoring is implemented, then loss of information is reduced, but data processing and analysis complexity increases
Solution Approach 1:
The system automatically processes and analyzes the comprehensive sensor data using machine learning algorithms, eliminating the need for manual data processing. The algorithms independently determine concrete properties such as yield stress and slump from the raw sensor measurements, and automatically assess compliance with specifications, reducing the burden of data analysis while maintaining complete information utilization
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the quality control of construction materials by providing real-time data analysis, optimizing mix designs, and ensuring compliance with specifications, thereby reducing costs and improving construction efficiency.
Implementation Method 1
providing at least one of an accelerometer and a gyroscope attached to the drum
Implementation Method 2
providing a hydraulic pressure sensor measuring pressure of a hydraulic system associated with the drum
Implementation Method 3
providing a sensor to monitor an angular velocity of the drum
Implementation Method 4
establishing a first pressure measurement from a first diaphragm based pressure sensor in contact with a column of a construction material at a first position with respect to the column of the construction material; establishing a second pressure measurement from a second diaphragm based pressure sensor in contact with a column of a construction material at a second position with respect to the column of the construction material
Data Source
AI summary
With increasing demands for cost reductions, profitability, tighter construction deadlines and potential liabilities construction companies, raw material suppliers, infrastructure owners, etc. are seeking cost effective systems, method and processes relating to the quality control of said construction materials. Accordingly processes, systems and methods are disclosed relating to concrete and other construction materials such as automatic slump measurement, automatic load measurement, artificial intelligence—machine learning optimization of material mixes, and automatic ingestion of data from unstructured documents to provide data to artificial intelligence—machine learning processes.