Compressive strength prediction and composition design of structural lightweight concretes using machine learning methods

ABSTRACT: Introduction. Reducing the density, increasing the strength and other physical-technical characteristics of lightweight concretes are urgent tasks of modern building materials science. To solve them, it is necessary to consider new approaches to the development of compositions of cement systems using effective porous aggregates, binders, chemical and mineral additives, including different nanomodifiers (carbon nanotubes, fullerenes, nanoparticles of SiO2, Al2O3, Fe2O3 , etc.). The complexity of designing modified cement concretes is largely due to their multicomponent nature and a large number of parameters affecting the key characteristics of material. The qualitative solution of such multicriteria problems is possible with the complex implementation of rational physical and computational experiments using mathematical modeling and computer technology. New opportunities for modeling of structure formation processes and predicting properties of multicomponent building materials are emerging with the development of machine learning methods. The purpose of this study is to develop machine learning algorithms that can efficiently establish quantitative dependences for the compressive strength of modified lightweight concretes on their composition, as well as to identify the optimal variation ranges of prescription parameters based on the obtained multifactor models to achieve the required level of controlled mechanical characteristic. Methods and materials. The processing and analysis of experimental research results were carried out using modern methods of machine learning with a teacher used in the problems of regression recovery, knowledge extraction and forecasting. To implement the developed machine learning algorithms, libraries in the Python programming language, in particular NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn, were used. Results and discussion. It is established that the gradient boosting model is the most accurate type among the obtained machine learning models. It is characterized by the following quality metrics: R2 = 0.9557; MAE = 2.4847; MSE = 12.7704; RMSE=3.5736; MAPE = 11.1813%. According to the analysis of this multifactor model, the optimal dosages of pozzolanic and expanding modifiers amounted to 4.5–6.0% and 6.0–7.5% of the binder weight (Portland cement + modifier), respectively, which ensured achievement of the required level of compressive strength (40–70 MPa) of lightweight concretes at the age of 28 days at material density reduced by 3–10% (the range under consideration is 1200–1900 kg/m3). Conclusions. Thus, the study results show the prospects of using machine learning methods for design compositions and predicting properties of multicomponent cement system

Morphometrical indices on temporomandibular joint in patients with facial asymmetry and prognathia

Отримані результати показали, що у групах з ліво- та правосторонньою асиметріями спостерігаються достовірні підвищення крутизни суглобового горбика порівняно з відповідним показником протилежної сторони. Величина переднього суглобового простору також була більш вузькою на стороні асиметрії, тимчасом як різниці в розмірах заднього суглобового простору не спостерігалося, що є свідченням переднього положення відростка нижньої щелепи в суглобовій ямці СНЩС на стороні асимерії. Розрахунок положення голівки відростка показав його передню локалізацію на стороні асиметрії та задню на протилежній стороні. Показники положення достовірно ідрізнялися від даних у групі контролю.In both groups with the right and left asymmetrical state of facial skeleton temporo-mandibular joint (TMJ) on the deviated side showed a significally steeper eminence at the contrlateral side (p<0.05). The anterior joint space was narrower on the side of asymmetry than on the contrlateral side whereas the posterior joint space did not differ markedly. The marked anterior location of the head of condilus was noted on the side of asymmetry. While posterior one was observed on the contrlateral side. Both locations were significantly different from control data (p<0.05)

The choice of lower jaw immobilization method in patients with acute TMJ arthritis

The adequate choice of lower jaw immobilization method is a very important point in patients with acute inflammation of TMJ complex treatment. The most widely spread methods (lower jaw supporting bandage, ligature binding, bimaxial wire splint immobilization) have some essential drawbacks: low efficiency, teeth shake loosing, technical complication, much time expenditure. The investigation is grounded of 36 cases of acute TMJ arthritis in patients of 18-55 years old who were treated at the clinical department of Prosthetic Chair, Odessa State Medical University. 16 patients have been treated with the use of a very simple, accessible in ambulatory practice method that provides reliable fixation of lower jaw and joint unloading. The percentage of full recovery in this group of patients put together 93,8% (P<0,05)

A phase-field/Monte-Carlo model describing organic crystal growth from solution

68.35.Ct Interface structure and roughness, 68.43.Mn Adsorption kinetics, 68.55.Ac Nucleation and growth: microscopic aspects,