Real-time prediction of early concrete compressive strength using AI and hydration monitoring.

The continuous evolution of construction technologies, particularly in reinforced concrete production, demands advanced, reliable, and efficient methodologies for real-time monitoring and prediction of concrete compressive strength. Traditional laboratory methods for assessing compressive strength are time-intensive and can introduce delays in construction workflows. This study introduces a comprehensive framework for a system designed to predict early-age compressive strength of concrete through continuous monitoring of the cement hydration process using a custom artificial intelligence (AI) model.

Influence of Optimization Algorithms and Computational Complexity on Concrete Compressive Strength Prediction Machine Learning Models for Concrete Mix Design.

The proper design of concrete mixtures is a critical task in concrete technology, where optimal strength, eco-friendliness, and production efficiency are increasingly demanded. While traditional analytical methods, such as the Three Equations Method, offer foundational approaches to mix design, they often fall short in handling the complexity of modern concrete technology. Machine learning-based models have demonstrated notable efficacy in predicting concrete compressive strength, addressing the limitations of conventional methods.

Computational Complexity and Its Influence on Predictive Capabilities of Machine Learning Models for Concrete Mix Design.

The design of concrete mixtures is crucial in concrete technology, aiming to produce concrete that meets specific quality and performance criteria. Modern standards require not only strength but also eco-friendliness and production efficiency. Based on the Three Equation Method, conventional mix design methods involve analytical and laboratory procedures but are insufficient for contemporary concrete technology, leading to overengineering and difficulty predicting concrete properties.

Model-Based Adaptive Machine Learning Approach in Concrete Mix Design.

Concrete mix design is one of the most critical issues in concrete technology. This process aims to create a concrete mix which helps deliver concrete with desired features and quality. Contemporary requirements for concrete concern not only its structural properties, but also increasingly its production process and environmental friendliness, forcing concrete producers to use both chemically and technologically complex concrete mixtures.

Concrete Compressive Strength under Changing Environmental Conditions during Placement Processes.

The technological process of concrete production consists of several parts, including concrete mix design, concrete mix production, transportation of fresh concrete mix to a construction site, placement in concrete framework, and curing. Proper execution of these steps provides good quality concrete. Some factors can disturb the technological process, mainly temperature and excessive precipitation.

Framework for Structural Health Monitoring of Steel Bridges by Computer Vision.

The monitoring of a structural condition of steel bridges is an important issue. Good condition of infrastructure facilities ensures the safety and economic well-being of society. At the same time, due to the continuous development, rising wealth of the society and socio-economic integration of countries, the number of infrastructural objects is growing.

Machine Learning Techniques in Concrete Mix Design.

Concrete mix design is a complex and multistage process in which we try to find the best composition of ingredients to create good performing concrete. In contemporary literature, as well as in state-of-the-art corporate practice, there are some methods of concrete mix design, from which the most popular are methods derived from The Three Equation Method. One of the most important features of concrete is compressive strength, which determines the concrete class.

The Application of Small-Angle Light Scattering for Rheo-Optical Characterization of Chitosan Colloidal Solutions.

In the recent studies on chitosan hydrogels, it was found that understanding both rheological and structural properties plays an important role in their application. Therefore, a combination of two independent techniques was applied to investigate micro- and macroscopic properties of chitosan colloidal system. Studies on viscous properties, as well as the sol-gel phase transition process, were performed using rheological methods coupled with the small angle light scattering (SALS) technique.

Deformation Analysis of a Composite Bridge during Proof Loading Using Point Cloud Processing.

Remote sensing in structural diagnostics has recently been gaining attention. These techniques allow the creation of three-dimensional projections of the measured objects, and are relatively easy to use. One of the most popular branches of remote sensing is terrestrial laser scanning.

The effects of sucrose on the sol-gel phase transition and viscoelastic properties of potato starch solutions.

In this paper, starch pastes in the form of solutions and gels were investigated to determine viscoelastic properties and sol-gel phase transition temperatures using rheological methods. The gelatinization process was carried out at a temperature of 95 °C with the use of a pressureless starch cell with a stirrer. Starch pastes obtained were used to determine rheological properties under isothermal conditions (in the temperature range of 45-25 °C) by a cone-plate measurement system.

Rheo-Kinetic Study of Sol-Gel Phase Transition of Chitosan Colloidal Systems.

Chitosan colloidal systems, created by dispersing in aqueous solutions of hydrochloric acid, with and without the addition of disodium β-glycerophosphate (β-NaGP), were prepared for the investigation of forming mechanisms of chitosan hydrogels. Three types of chitosan were used in varying molecular weights. The impacts of the charge and shape of the macromolecules on the phase transition process were assessed.