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. Machine learning-based methods offer a solution, as they have proven effective in predicting concrete compressive strength for concrete mix design. This paper scrutinises the association between the computational complexity of machine learning models and their proficiency in predicting the compressive strength of concrete. This study evaluates five deep neural network models of varying computational complexity in three series. Each model is trained and tested in three series with a vast database of concrete mix recipes and associated destructive tests. The findings suggest a positive correlation between increased computational complexity and the model's predictive ability. This correlation is evidenced by an increment in the coefficient of determination (R2) and a decrease in error metrics (mean squared error, Minkowski error, normalized squared error, root mean squared error, and sum squared error) as the complexity of the model increases. The research findings provide valuable insights for increasing the performance of concrete technical feature prediction models while acknowledging this study's limitations and suggesting potential future research directions. This research paves the way for further refinement of AI-driven methods in concrete mix design, enhancing the efficiency and precision of the concrete mix design process.
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. The concrete mix design methods currently used in engineering practice are joint analytical and laboratory procedures derived from the Three Equation Method and do not perform well enough for the needs of modern concrete technology. This often causes difficulties in predicting the final properties of the designed mix and leads to precautionary oversizing of concrete properties for fear of not providing the required parameters. A new approach that would make it possible to predict the newly designed concrete mix properties is highly desirable. The answer to this challenge can be methods based on machine learning, which have been intensively developed in recent years, especially in predicting concrete compressive strength. Machine learning-based methods have been more or less successful in predicting concrete compressive strength, but they do not reflect well the variability that characterises the currently used concrete mixes. A new adaptive solution that allows estimating concrete compressive strength on the basis of the concrete mix main ingredient composition by including two observations for a given batch of concrete is proposed herein. In presented study, a machine learning model was built with a deep neural network architecture, trained on an extensive database of concrete recipes, and translated into a mathematical formula. Testing on four concrete mix recipes was performed, which were calculated according to contemporary design methods (Bolomey and Fuller method), and a comparative analysis was conducted. It was found out that the new algorithm performs significantly better than that without adaptive features trained on the same dataset. The presented algorithm can be used as a concrete strength checking tool for the concrete mix design process.
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. Changing daily temperature and rainfall during fabrication, transportation, and placement can shape not only the properties of the concrete mix but also the compressive strength of hardened concrete. In this paper, we tried to answer the question of how temperature and precipitation affect concrete production. The scope of this study was to determine the change of compressive strength of the hardened concrete in a specific period for selected concrete mix recipes, taking into account changing daily temperature and precipitation magnitude. The investigated concrete mixes concrete compressive strength beyond that of the concrete grade, termed "concrete superstrength". This concrete post limiting behaviour of concrete is also discussed.
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. Therefore, there is a need to introduce an easy-to-use and relatively low-cost method of bridge diagnostics. We can achieve these benefits by the use of Unmanned Aerial Vehicle-Based Remote Sensing and Digital Image Processing. In our study, we present a state-of-the-art framework for Structural Health Monitoring of steel bridges that involves literature review on steel bridges health monitoring, drone route planning, image acquisition, identification of visual markers that may indicate a poor condition of the structure and determining the scope of applicability. The presented framework of image processing procedure is suitable for diagnostics of steel truss riveted bridges. In our considerations, we used photographic documentation of the Fitzpatrick Bridge located in Tallassee, Alabama, USA.
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. Predictable compressive strength of concrete is essential for concrete structure utilisation and is the main feature of its safety and durability. Recently, machine learning is gaining significant attention and future predictions for this technology are even more promising. Data mining on large sets of data attracts attention since machine learning algorithms have achieved a level in which they can recognise patterns which are difficult to recognise by human cognitive skills. In our paper, we would like to utilise state-of-the-art achievements in machine learning techniques for concrete mix design. In our research, we prepared an extensive database of concrete recipes with the according destructive laboratory tests, which we used to feed the selected optimal architecture of an artificial neural network. We have translated the architecture of the artificial neural network into a mathematical equation that can be used in practical applications.
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. The viscoelastic behavior of the tested medium was confirmed. The range of gelation temperatures was determined and the influence of several factors was discussed, e.g. the effect of sucrose addition and cooling rate on the phenomenon of sol-gel phase transition. In addition, mechanical properties of the obtained starch gel structures were determined using a fractional rheological model.
Solanum tuberosum , Starch/chemistry , Sucrose/chemistry , Gels , Phase Transition , Rheology , Solutions , Temperature , Viscosity
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. Laser scanners are fast and efficient, gathering up to one million points per second. However, the weakness of terrestrial laser scanning is the troublesome processing of point clouds. Currently, many studies deal with the subject of point cloud processing in various areas, but it seems that there are not many clear procedures that we can use in practice, which indicates that point cloud processing is one of the biggest challenges of this issue. To tackle that challenge we propose a general framework for studying the structural deformations of bridges. We performed an advanced object shape analysis of a composite foot-bridge, which is subject to spatial deformations during the proof loading process. The added value of this work is the comprehensive procedure for bridge evaluation, and adaptation of the spheres translation method procedure for use in bridge engineering. The aforementioned method is accurate for the study of structural element deformation under monotonic load. The study also includes a comparative analysis between results from the spheres translation method, a total station, and a deflectometer. The results are characterized by a high degree of convergence and reveal the highly complex state of deformation more clearly than can be concluded from other measurement methods, proving that laser scanning is a good method for examining bridge structures with several competitive advantages over mainstream measurement methods.
Chitosan colloidal systems, created by dispersing in aqueous solutions of hydrochloric acid, with and without the addition of disodium ß-glycerophosphate (ß-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. The chitosan system without the addition of ß-NaGP was characterized by stiff and entangled molecules, in contrast to the chitosan system with the addition of ß-NaGP, wherein the molecules adopt a more flexible and disentangled form. Differences in molecules shapes were confirmed using the Zeta potential and thixotropy experiments. The chitosan system without ß-NaGP revealed a rapid nature of phase transition-consistent with diffusion-limited aggregation (DLA). The chitosan system with ß-NaGP revealed a two-step nature of phase transition, wherein the first step was consistent with reaction-limited aggregation (RLA), while the second step complied with diffusion-limited aggregation (DLA).
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. Based on the anisotropy of scattering patterns obtained during rotational shear tests, it was found that the chitosan solution reveals two different behaviors delimited by the critical value of the shear rate. Below a critical value, chitosan clusters are deformed without breaking up aggregates, whereas after exceeding a critical value, chitosan clusters apart from deformation also breakup into smaller aggregates. The values of the radius of gyration determined by applying the Debye function allow one to state that with an increase of chitosan concentration, molecule size decreases. An analysis of the light scattering data from the temperature ramp test showed that with an increase of temperature, the level of polymer coil swelling increases. Simultaneously, the supply of thermal energy leads to a neutralization of the charge of chitosan chains. As a consequence, the formation of intermolecular links occurs and a gel structure is formed.
