Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
12,714
result(s) for
"Mortars"
Sort by:
Modeled and Measured Carbon Isotopic Composition and Petrographically Estimated Binder-Aggregate Ratio—Recipe for Binding Material Dating?
This paper presents the results of radiocarbon (14C) dating of bulk mortars and reports an attempt of implementation of the knowledge about the isotopic fractionation, based on δ13C measurements, to make the age correction for mortars, together with verification of such correction based on the percentage estimation of carbonate components, namely binder and aggregate. To evaluate the variability of isotopic fractionation during CO2 absorption by mortar, dependent on the climatic and environmental conditions, and the type of mortar, the δ13C measurements have been performed for the mortars from Sussita (Golan Heights). Such measurements were also made for fragments of natural carbonate rocks and for mortars produced in the laboratory from the same substrate. We propose the recipe for mortars age estimation.
Journal Article
Lavender and Black Pine Waste as Additives Enhancing Selected Mechanical and Hygrothermal Properties of Cement Mortars
The paper presents the mechanical and hygrothermal properties of cement mortars containing bio-powders made from lavender waste and black pine wood. The wastes were mechanically ground with a hammer mill to a fraction not exceeding 0.5 mm and then dried in air-dry conditions. The influence of bio-additives in amounts of 1.5% and 2.5% of the overall mortar volume was tested. The aim of the paper was to determine the impact of bio-additives on the mechanical and hygrothermal properties of the tested cement mortars. This publication included tests of compressive and flexural strength, elastic modulus, water absorption, absorption due to capillary rise, sorption and desorption properties, thermal properties, microstructural tests using mercury intrusion porosimetry and SEM, and EDS. The main conclusions of the research indicate that mortars with both 1.5% and 2.5% bio-powders are characterized by strong bactericidal properties, lower sorption properties at high air humidity, lower thermal conductivity, reduced compressive strength by 22–27%, no significant effect on the flexural strength, and significant reduction in capillary action of mortars both with short-term and long-term water exposure.
Journal Article
Enhancing Clay-Based 3D-Printed Mortars with Polymeric Mesh Reinforcement Techniques
Additive manufacturing (AM) technologies, including 3D mortar printing (3DMP), 3D concrete printing (3DCP), and Liquid Deposition Modeling (LDM), offer significant advantages in construction. They reduce project time, costs, and resource requirements while enabling free design possibilities and automating construction processes, thereby reducing workplace accidents. However, AM faces challenges in achieving superior mechanical performance compared to traditional methods due to poor interlayer bonding and material anisotropies. This study aims to enhance structural properties in AM constructions by embedding 3D-printed polymeric meshes in clay-based mortars. Clay-based materials are chosen for their environmental benefits. The study uses meshes with optimal geometry from the literature, printed with three widely used polymeric materials in 3D printing applications (PLA, ABS, and PETG). To reinforce the mechanical properties of the printed specimens, the meshes were strategically placed in the interlayer direction during the 3D printing process. The results show that the 3D-printed specimens with meshes have improved flexural strength, validating the successful integration of these reinforcements.
Journal Article
A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength
Despite the extensive use of mortars materials in constructions over the last decades, there is not yet a reliable and robust method, available in the literature, which can estimate its strength based on its mix parameters. This limitation is due to the highly nonlinear relation between the mortar’s compressive strength and the mixed components. In this paper, the application of artificial intelligence techniques toward the prediction of the compressive strength of cement-based mortar materials with or without metakaolin has been investigated. Specifically, surrogate models (such as artificial neural network, ANN and adaptive neuro-fuzzy inference system, ANFIS models) have been developed to the prediction of the compressive strength of mortars trained using experimental data available in the literature. The comparison of the derived results with the experimental findings demonstrates the ability of both ANN and ANFIS models to approximate the compressive strength of mortars in a reliable and robust manner. Although ANFIS was able to obtain higher performance prediction to estimate the compressive strength of mortars compared to ANN model, it was found through the verification process of some other additional data, the ANFIS model has overfitted the data. Therefore, the developed ANN model has been introduced as the best predictive technique for solving problem of the compressive strength of mortars. Furthermore, using the optimum developed model an ambitious attempt to reveal the nature of mortar materials has been made.
Journal Article
Mortars with Mining Tailings Aggregates: Implications for Additive Manufacturing
There is no doubt that additive manufacturing (AM) with mortars presents an opportunity within the framework of a circular economy that should not be overlooked. The concepts of reduce, reuse, and recycle are fully aligned with this technology. One of the less explored possibilities is the utilisation of mining tailings as aggregates in printing mortars. This idea not only incorporates the concept of recycling but also contributes to a reduction in the production of potentially hazardous waste that would otherwise require storage in dams, thereby decreasing long-term environmental risks and improving the management of mineral resources. We employed a mortar composed of 12.5% material derived from mining tailings to highlight aspects of AM that are typically not subject to analysis, such as the necessity of considering contact interfaces between layers in structural design, the stackability of layers during the construction process, and the behaviour under fire and seismic events, which must be taken into account during the operational phase. Without aiming for exhaustiveness, we conducted a series of tests and computational modelling to show the significance of these factors, with the intention of drawing the attention of different stakeholders—including construction companies, regulatory authorities, standardisation agencies, insurers, and end-users.
Journal Article
Assessment of the Applicability of Selected Data Mining Techniques for the Classification of Mortars Containing Recycled Aggregate
The article contains the results of selected tests of physical and mechanical properties of mortars differentiated in terms of the binder used: cement, epoxy, epoxy modified with PET waste glycolysate and polyester. Each type of mortar was modified by partial (0–20% vol.) substitution of sand with an agglomerate made from waste polyethylene. The obtained results were used to build a database of mortar properties, which was then analyzed with the use of three different techniques of knowledge extraction from databases, i.e., cluster analysis, decision trees and discriminant analysis. The average results of the properties tested were compared, taking into account the type of mortar, indicating those with the most favorable parameters. The possibilities and correctness of mortar classification with the use of the indicated “data mining” methods were compared. The results obtained confirmed that it is possible to successfully apply these methods to the classification of construction mortars and then to propose mortars with such a composition that will guarantee that the composite will have the expected properties. Both the presented method of plastic waste management and the proposed statistical approach are in line with the assumptions of the currently important concept of sustainable development in construction.
Journal Article
The State-of-the-Art of Dating Techniques Applied to Ancient Mortars and Binders: A Review
The most recent workshop on mortar dating (25–27 Oct. 2018, Bordeaux, Montaigne University, France), which closely followed the publication of an extensive round robin-exercise involving several laboratories, was an opportunity to review the history and challenges of mortar dating methods and procedures currently in use. This review stems from the keynote lectures presented at the meeting, and wishes to summarize recent results, present trends, and future challenges. Three major areas are brought into focus (1) radiocarbon (14C) dating of complex mortars: can we assess the chances of successful dating?, (2) 14C dating of archaeological carbonate materials: difficulties, new directions and applications, and (3) single grain optically stimulated luminescence (OSL) dating of mortars in architectural archaeology: the current state of the art. This paper reflects the material presented by the authors and discussed at the workshop.
Journal Article
Radiocarbon Dating of Mortars and Charcoals from Novae Bath Complex: Sequential Dissolution of Historical and Experimental Mortar Samples with Pozzolanic Admixture
Carbonaceous mortars from Novae (Bulgaria) contain local loess, crushed bricks and ceramic dust (pozzolanic materials). The reaction between lime and pozzolanic additives occurs easily and affects the rate and course of leaching reaction of carbonates in orthophosphoric acid during the sample pretreatment for dating. The composition of the Bulgarian mortars does not allow for unambiguous conclusions about chronology, but together with the observations of experimental mortars, gives new guidelines in terms of pozzolanic mortar application for dating. The presented research illustrates the possible reasons of difficulties with obtaining the appropriate portion of gas for radiocarbon (14C) measurement. To verify the relative chronology of legionary baths complex in Novae, the charcoals samples were also dated in addition to the mortar.
Journal Article
Prediction of cement-based mortars compressive strength using machine learning techniques
The application of artificial neural networks in mapping the mechanical characteristics of the cement-based materials is underlined in previous investigations. However, this machine learning technique includes several major deficiencies highlighted in the literature, such as the overfitting problem and the inability to explain the decisions. Hence, the present study investigates the applicability of other common machine learning techniques, i.e., support vector machine, random forest (RF), decision tree, AdaBoost and k-nearest neighbors in mapping the behavior of the compressive strength (CS) of cement-based mortars. To this end, a big experimental database has been compiled based on experimental data available in the literature considering, namely the cement grade, which is an important parameter for the modeling of mortar’s CS. Other important parameters are namely the age, the water-to-binder ratio, the particle size distribution of the sand and the amount of plasticizer. Many models based on the influential factors affecting machine learning techniques have been developed, and their prediction capacities have been assessed using performance indexes. The present research highlights the potential of AdaBoost and RF models as useful tools which can assist in mortar design and/or optimization. In addition, mapping the development of mortar characteristics can assist in revealing the influence of the different mortar mix parameters on the compressive strength.
Journal Article