Adaptive Mesh Strategy for Efficient Use of Interface Elements in a 3D Probabilistic Explicit Cracking Model for Concrete.

In this paper, the development of a 3D adaptive probabilistic explicit cracking model for concrete is reported. The contribution offered herein consists in a new adaptive mesh strategy designed to optimize the use of interface elements in probabilistic explicit cracking models. The proposed adaptive mesh procedure is markedly different from other strategies found in the literature, since it takes into account possible influences on the redistribution of stresses after cracking and can also be applied to purely deterministic cracking models. The process of obtaining the most appropriate adaptive mesh procedure involved the development and evaluation of three different adaptivity strategies. Two of these adaptivity strategies were shown to be inappropriate due to issues related to stress redistribution after cracking. The validation results demonstrate that the developed adaptive probabilistic model is capable of predicting the scale effect at a level similar to that experimentally observed, considering the tensile failure of plain concrete specimens. The results also show that different softening levels can be obtained. The proposed adaptive mesh strategy proved to be advantageous, being able to promote significant reductions in the simulation time in comparison with the classical strategy commonly used in probabilistic explicit cracking models.

Comparison of calcium carbonate production by bacterial isolates from recycled aggregates.

The new technology of microbially induced calcium carbonate precipitation (MICP) has been applied in construction materials as a strategy to enhance their properties. In pursuit of solutions that are more localized and tailored to the study's target, this work focused on isolating and selecting bacteria capable of producing CaCO3 for posterior application in concrete aggregates. First, eleven bacterial isolates were obtained from aggregates and identified as genera Bacillus, Lysinibacillus, Exiguobacterium, and Micrococcus. Then, the strains were compared based on the quantity and nature of calcium carbonate they produced using thermogravimetric analysis, X-ray diffraction, and scanning electron microscopy with energy dispersive spectroscopy. Bacillus sp. dominated the cultured isolates and, along with Lysinibacillus sp., exhibited the highest CaCO3 conversion (up to 80%). On the other hand, Exiguobacterium and Micrococcus genera showed the poor ability to MICP (21.3 and 20.3%, respectively). Calcite and vaterite were the dominant carbonate polymorphs, with varying proportions. Concrete aggregates have proven to be a source of microorganisms capable of producing stable calcium carbonates with a high conversion rate. This indicates the feasibility of using microorganisms derived from local sources for application in construction materials as a sustainable way to enhance their characteristics.

A New Sustainable PPT Coating Based on Recycled PET to Improve the Durability of Hydraulic Concrete.

A new, sustainable polypropylene terephthalate (PPT) coating was synthesized from recycled polyethylene terephthalate (PET) and applied onto a hydraulic concrete substrate to improve its durability. For the first step, PET bottle wastes were ground and depolymerized by glycolysis using propylene glycol (PG) in a vessel-type reactor (20-180 °C) to synthesize bis(2-hydroxypropyl)-terephthalate (BHPT), which was applied as a coating to one to three layers of hydraulic concrete substrate using the brushing technique and polymerized (150 °C for 15 h) to obtain PPT. PET, BHPT, and PPT were characterized by FT-IR, PET, and PPT using TGA, and the PPT coatings by SEM (thickness), ASTM-D3359-17 (adhesion), and water contact angle (wettability). The durability of hydraulic concrete coated with PPT was studied using resist chloride ion penetration (ASTM-C1202-17), carbonation depth at 28 days (RILEM-CPC-18), and the absorption water ratio (ASTM-C1585-20). The results demonstrated that the BHPT and PPT were synthetized (FT-IR), and PPT had a similar thermal behavior to PET (TGA); the PPT coatings had good adhesion to the substrate, with thicknesses of micrometric units. PPT coatings presented hydrophilic hydrophilic behavior like PET coatings, and the durability of hydraulic concrete coated with PPT (2-3 layers) improved (migration of chloride ions decreased, carbonation depth was negligible, and the absorption water ratio decreased).

Sustainable Hybrid Lightweight Aggregate Concrete Using Recycled Expanded Polystyrene.

Global concrete production, reaching 14×1013m3/year, raises environmental concerns due to the resource-intensive nature of ordinary Portland cement (OPC) manufacturing. Simultaneously, 32.7×109 kg/year of expanded polystyrene (EPS) waste poses ecological threats. This research explores the mechanical behavior of lightweight concrete (LWAC) using recycled EPS manufactured with a hybrid cement mixture (OPC and alkali-activated cement). These types of cement have been shown to improve the compressive strength of concrete, while recycled EPS significantly decreases concrete density. However, the impact of these two materials on the LWAC mechanical behavior is unclear. LWAC comprises 35% lightweight aggregates (LWA)-a combination of EPS and expanded clays (EC) - and 65% normal-weight aggregates. As a cementitious matrix, this LWAC employs 30% OPC and 70% alkaline-activated cement (AAC) based on fly ash (FA) and lime. Compressive strength tests after 28 curing days show a remarkable 48.8% improvement, surpassing the ACI 213R-03 standard requirement, which would allow this sustainable hybrid lightweight aggregate concrete to be used as structural lightweight concrete. Also obtained was a 21.5% reduction in density; this implies potential cost savings through downsizing structural elements and enhancing thermal and acoustic insulation. X-ray diffraction (XRD) and Fourier transform infrared (FTIR) spectroscopy reveal the presence of C-S-H, C-(A)-S-H, and N-A-S-H gels. However, anhydrous products in the hybrid LWAC suggest a slower reaction rate. Further investigation into activator solution dosage and curing temperature is recommended for improved mechanical performance on the 28th day of curing. This research highlights the potential for sustainable construction incorporating waste and underscores the importance of refining activation parameters for optimal performance.

The Influence of Materials on the Mechanical Properties of Ultra-High-Performance Concrete (UHPC): A Literature Review.

Ultra-high-performance concrete (UHPC) is a cementitious composite combining high-strength concrete matrix and fiber reinforcement. Standing out for its excellent mechanical properties and durability, this material has been widely recognized as a viable choice for highly complex engineering projects. This paper proposes (i) the review of the influence exerted by the constituent materials on the mechanical properties of compressive strength, flexural tensile strength, and elastic modulus of UHPC and (ii) the determination of optimal quantities of the constituent materials based on simplified statistical analyses of the developed database. The data search was restricted to papers that produced UHPC with straight steel fibers at a content of 2% by volume. UHPC mixture models were proposed based on graphical analyses of the relationship of constituent materials versus mechanical properties, aiming to optimize the material's performance for each mechanical property. The results proved to be in accordance with the specifications present in the literature, characterized by high cement consumption, significant presence of fine materials, and low water-to-binder ratio. The divergences identified between the mixtures reflect how the constituent materials uniquely impact each mechanical property of the concrete. In general, fine materials were shown to play a significant role in increasing the compressive strength and flexural tensile strength of UHPC, while water and superplasticizers stood out for their influence on the material's workability.

Effect of Graphene Oxide Surface Deposition Process on Synthetic Macrofibers and Its Results on the Microstructure of Fiber-Reinforced Concrete.

The improvement of the mechanical properties of concrete can be achieved with the use of synthetic macrofibers. However, this fiber-matrix interaction will be sufficiently efficient for tensile efforts only when there is a binding agent that associates the characteristics of the paste with the characteristics of the surface of the reinforcing material. As already identified, in a first phase of this research using synthetic microfibers, a better fiber-matrix interaction can be achieved with the surface treatment of synthetic fibers with graphene oxide. In this way, we sought to evaluate the surface treatment with graphene oxide on two synthetic polypropylene macrofibers (macrofiber "A" and macrofiber "B") and its contribution to the concrete transition zone. The surface deposition on the macrofiber was carried out using the ultrasonication method; then, the macrofiber with the best deposition for creating reinforced concrete mixtures was identified. To evaluate the quality of GO deposition, scanning electron microscopy (SEM-FEG) and energy-dispersive spectroscopy (EDS) tests were carried out; the same technique was used to evaluate the macrofiber-matrix transition zone. The SEM-FEG images indicated that macrofiber "B" obtained greater homogeneity in surface deposition and it presented a 13% greater deposition of C in the EDS spectra. The SEM-FEG micrographs for reinforced concrete indicated a reduction in voids in the macrofiber-matrix transition zone for concretes that used macrofibers treated with GO.

Dataset used to develop soft computing models that predict the stiffness modulus of bituminous mixtures.

This data article presents information on the measurement of Indirect Tensile Stiffness Modulus of laboratory and field asphalt mixtures. The asphalt mixes are composed of three distinct binders that were categorised by their penetration grade (40/55-TLA, 60/75-TLA, and 60/70-MB) and aggregates (limestone, sharp sand, and filler). The asphalt mixtures are called dense-graded hot mix asphalt (HMA) and gap-graded stone matrix asphalt (SMA). The variables in the dataset were selected in accordance with the specifications of the dynamic modulus models that are currently in use as well as the needs for the quality control and assurance (QC & QA) assessment of asphalt concrete mixes. The data parameters included are temperature, asphalt content, and binder viscosity, air void content, cumulative percent retained on 19, 12.5, and 4.75 mm sieves, maximum theoretical specific gravity, aggregate passing #200 sieve, effective asphalt content, density, flow, marshal stability, coarse-to-fine particle ratio and the Indirect Tensile Stiffness Modulus (ITSM). Utilising soft computing techniques, models were developed utilising the data thus eliminating the requirement for complex and time-consuming laboratory testing.

Influence of Service Conditions and Mix Design on the Physical-Mechanical Properties of Roller-Compacted Concrete for Pavement.

This research focuses on the behavior of roller-compacted concrete (RCC) used in pavements, which are prone to deterioration affecting their performance. These deteriorations result from various causes, including traffic load, errors during construction, mix design, and ambient conditions. Among these, ambient conditions could lead to a marked variable impact on material behavior and durability depending on the conditions associated with each region. Accordingly, this study aims to deepen the understanding of the effect, which a broader range of ambient conditions and different mix designs have on the physical and mechanical properties of RCC. Measurements such as the amount of water vapor per kilogram of air were used to apply the findings comprehensively. The RCC analysis encompassed experimentation with different compositions, altering the cement water ratio amount, and adding a superplasticizer. The impact of curing on the materials was evaluated before subjecting them to various humidity and temperature conditions. Laboratory tests were conducted to measure performance, including moisture, shrinkage, compressive strength, and the progression of flexural fracture resistance over curing periods of up to 90 days. The results revealed a logarithmic correlation between shrinkage and ambient humidity, which is the most determining factor in performance. Mix optimization through increased cement and reduced water enhanced the tensile strength of the material. Furthermore, the curing process was confirmed to increase resistance to shrinkage, especially in the long term, establishing it as a crucial element for the structural stability of RCC, which is relatively insensitive to variations in ambient conditions.

Prediction of the Physical-Mechanical Properties of Roller-Compacted Concrete Pavements under Different Service and Mix Conditions Based on Cement and Water Content.

Roller-compacted concrete (RCC) for pavements has experienced problems with its physical-mechanical performance over extended periods due to ambient and in situ curing conditions. Accordingly, this study aimed to present multiple regression equations for calculating the physical-mechanical properties of RCC for pavements under different service and mix conditions. For this purpose, the research included two cement and two water contents, one reduced with admixture, and four combinations of temperature and relative humidity. For model calibration and definition of the equations, cubic and beam samples were fabricated to carry out physical-mechanical tests, such as moisture content, shrinkage, and modulus of rupture. Laboratory-obtained data were studied with the Response Surface Methodology (RSM) to determine the best regression equations. The main findings determined that the behavior of a mixture of RCC at a prolonged ambient exposure time is possible because the surface models and the RSM were consistent with the different service and mix conditions. The models showed an accuracy of 98.99% in detecting shrinkage changes from 12 to 16% cement with 5.65% water in dry to wet ambient conditions. Similarly, moisture content and modulus of rupture had a 98.27 to 98.88% fit. Finally, the drying shrinkage, with mixes of 12% cement and water content variations with water-reducing admixture and superplasticizer effects, had an adjustment of 94.87%.

ConcreteXAI: A multivariate dataset for concrete strength prediction via deep-learning-based methods.

Concrete is a prominent construction material globally, owing to its reputed attributes such as robustness, endurance, optimal functionality, and adaptability. Formulating concrete mixtures poses a formidable challenge, mainly when introducing novel materials and additives and evaluating diverse design resistances. Recent methodologies for projecting concrete performance in fundamental aspects, including compressive strength, flexural strength, tensile strength, and durability (encompassing homogeneity, porosity, and internal structure), exist. However, actual approaches need more diversity in the materials and properties considered in their analyses. This dataset outlines the outcomes of an extensive 10-year laboratory investigation into concrete materials involving mechanical tests and non-destructive assessments within a comprehensive dataset denoted as ConcreteXAI. This dataset encompasses evaluations of mechanical performances and non-destructive tests. ConcreteXAI integrates a spectrum of analyzed mixtures comprising twelve distinct concrete formulations incorporating diverse additives and aggregate types. The dataset encompasses 18,480 data points, establishing itself as a cutting-edge resource for concrete analysis. ConcreteXAI acknowledges the influence of artificial intelligence techniques in various science fields. Emphatically, deep learning emerges as a precise methodology for analyzing and constructing predictive models. ConcreteXAI is designed to seamlessly integrate with deep learning models, enabling direct application of these models to predict or estimate desired attributes. Consequently, this dataset offers a resourceful avenue for researchers to develop high-quality prediction models for both mechanical and non-destructive tests on concrete elements, employing advanced deep learning techniques.

Thermal Conductivity in Concrete Samples with Natural and Synthetic Fibers.

One crucial property of concrete, particularly in construction, is its thermal conductivity, which impacts heat transfer through conduction. For example, reducing the thermal conductivity of concrete can lead to energy savings in buildings. Various techniques exist for measuring the thermal conductivity of materials, but there is limited discussion in the literature about suitable methods for concrete. In this study, the transient line source method is employed to evaluate the thermal conductivity of concrete samples with natural and synthetic fibers after 7 and 28 days of curing. The results indicate that concrete with hemp fiber generally exhibits higher thermal conductivity values, increasing by 48% after 28 days of curing, while synthetic fibers have a minimal effect. In conclusion, this research opens the door to using natural alternatives like hemp fiber to improve concrete's thermal properties, providing alternatives for thermo-active foundations and geothermal energy piles which require high thermal conductivities.

Influence of the ANN Hyperparameters on the Forecast Accuracy of RAC's Compressive Strength.

The artificial neural networks (ANNs)-based model has been used to predict the compressive strength of concrete, assisting in creating recycled aggregate concrete mixtures and reducing the environmental impact of the construction industry. Thus, the present study examines the effects of the training algorithm, topology, and activation function on the predictive accuracy of ANN when determining the compressive strength of recycled aggregate concrete. An experimental database of compressive strength with 721 samples was defined considering the literature. The database was used to train, validate, and test the ANN-based models. Altogether, 240 ANNs were trained, defined by combining three training algorithms, two activation functions, and topologies with a hidden layer containing 1-40 neurons. The ANN with a single hidden layer including 28 neurons, trained with the Levenberg-Marquardt algorithm and the hyperbolic tangent function, achieved the best level of accuracy, with a coefficient of determination equal to 0.909 and a mean absolute percentage error equal to 6.81%. Furthermore, the results show that it is crucial to avoid the use of overly complex models. Excessive neurons can lead to exceptional performance during training but poor predictive ability during testing.

Exploring the Potential of Alternative Materials in Concrete Mixtures: Effect of Copper Slag on Mechanical Properties and Carbonation Resistance.

In this study, the effect on the flowability, compressive strength, absorption, sorptivity, and carbonation resistance of concrete with different copper slag (CS) replacement ratios was investigated. For this research, four concrete mixes with different percentages of CS were made (0%, 10%, 20%, and 30% of CS as replacement of cement by volume). In addition, the microstructure was analyzed by X-ray diffraction (XRD), scanning electron microscope (SEM), and thermogravimetric analysis (TG-DTG). The results shows that the incorporation of CS reduces the workability and compressive strength of the mixtures, being more significant in concrete with 30% CS. The carbonation depth of concrete with CS increases monotonically with increasing CS. In addition, the compressive strength of the carbonated (20% and 30% CS) concretes show a loss of compressive strength at 90 days of exposure when compared to their water-cured counterparts. The use of low percentages of CS does not generate a decrease in workability and its mechanical effect is not significant at prolonged ages, so the use of this waste as SCM in percentages close to 10% is a viable alternative to the sustainability of concrete and the management of this residue.

Post-Cracking Properties of Concrete Reinforced with Polypropylene Fibers through the Barcelona Test.

The Barcelona method was developed as an alternative to other tests for assessing the post-cracking behavior of fiber-reinforced concrete, with the main advantage being that it uses significantly smaller specimens compared to other methods. For this reason, it can provide a solution for characterizing concrete in hard-to-reach constructions such as roads and tunnels. On the other hand, polypropylene (PP) fibers have gained increased attention in recent years within the scientific community due to their high tensile strength and cost-effectiveness. This research aimed to understand the influence of PP fiber volume, slenderness (l/d), and reinforcement index on post-cracking properties of concrete, including toughness and residual strength (f_res), using the Barcelona method. Three fiber volumes, 0.4%, 0.8%, and 1.2%, and three slenderness ratios, 46.5, 58.1, and 69.8, were employed in normal-strength concrete. In addition to the reference mixture without fibers, 10 mixtures were prepared with 10 specimens each, resulting in a total of 100 specimens. Pearson's hypothesis test was employed to determine the existence of correlations between variables, followed by scatter plots to generate predictive equations between post-cracking properties and fiber attributes. The results indicated no direct correlation between fiber slenderness and post-cracking properties. Regarding fiber volume, there was a correlation with residual strength but not with toughness. However, the combined effect of volume and slenderness, the reinforcement index, correlates with the post-cracking properties of concrete. Finally, four predictive equations for toughness and residual strength were derived based on the reinforcement index. These equations can prove valuable for designing structures made of polypropylene fiber-reinforced concrete.

Assessment of Wood Bio-Concrete Properties Modified with Silane-Siloxane.

Bio-based materials, such as wood bio-concrete (WBC), hold promise in reducing energy consumption and carbon footprint of the construction industry. However, the durability of these materials is not well understood and can be negatively affected by the high water absorption capacity of wood bio-aggregates. In the field of cement composites, for example, silane-siloxane-based water repellent has been used to protect such materials from natural environmental attack. Nevertheless, there is still a limited understanding of various aspects related to this type of treatment, including its performance when applied to the bio-concrete substrate. This research aimed to investigate the influence of silane-siloxane on the rheology and hydration of cementitious paste through isothermal calorimetry and thermogravimetric analysis. Additionally, the impact of silane-siloxane on the physical and mechanical properties of WBCs was examined by conducting tests at fresh state (flow table and entrained air content) and hardened state (compressive strength and capillary water absorption). The composites were produced with a volumetric fraction of 45% of wood shavings while the cement matrix consisted of a combination of cement, rice husk ash, and fly ash. Silane-siloxane was applied in three ways: as coating, incorporated as an admixture, and in a combination of both methods. The results indicated that by incorporating silane in the cementitious pastethe viscosity increased by 40% and the hydration was delayed by approximately 6 h when compared to the reference. In addition, silane improved the compressive strength of WBCs by 24% when incorporated into the mixture, expressively reduced the water sorptivity of WBCs (93%), and was more effective if used as coating.

Unraveling the Interplay of Physical-Chemical Factors Impacting the Carbonation Performance of Recycled Aggregate Concrete.

Recycled aggregate concrete (RAC) includes recycled concrete aggregates (coarse and/or fine) as substitutes for natural aggregates as an approach to achieving a circular economy. Some concerns remain about its performance, including the carbonation resistance. The higher porosity of recycled concrete aggregates is logically a disadvantage, but the analysis must address many other factors. This paper provides an in-depth examination of recent advances in the carbonation performance of RAC. The emphasis is on factors that influence CO2 diffusion and the carbonation rate, e.g., the replacement ratio, source concrete quality, interfacial transition zone features, residual portlandite content, and porosity. The influences of previous treatments, combined action with supplementary cementitious materials, and loading conditions are also discussed. The replacement ratio has a significant impact on the carbonation performance of concrete, but it is also dependent on other factors. During carbonation, the physical effects of the porosity of the aggregate and the physical-chemical effects of the portlandite content in the adhered mortar are particularly important. The residual portlandite is especially significant because it is the primary hydration product responsible for the alkaline reserve for carbonation and the potential pozzolanic reaction, which are per se competing factors that determine the carbonation rate.

Machine Learning-Based Prediction of the Compressive Strength of Brazilian Concretes: A Dual-Dataset Study.

Lately, several machine learning (ML) techniques are emerging as alternative and efficient ways to predict how component properties influence the properties of the final mixture. In the area of civil engineering, recent research already uses ML techniques with conventional concrete dosages. The importance of discussing its use in the Brazilian context is inserted in an international context in which this methodology is already being applied, and it is necessary to verify the applicability of these techniques with national databases or what is created from national input data. In this research, one of these techniques, an artificial neural network (ANN), is used to determine the compressive strength of conventional Brazilian concrete at 7 and 28 days by using a database built through publications in congresses and academic works and comparing it with the reference database of Yeh. The data were organized into nine variables in which the data samples for training and test sets vary in five different cases. The eight possible input variables were: consumption of cement, blast furnace slag, pozzolana, water, additive, fine aggregate, coarse aggregate, and age. The response variable was the compressive strength of the concrete. Using international data as a training set and Brazilian data as a test set, or vice versa, did not show satisfactory results in isolation. The results showed a variation in the five scenarios; however, when using the Brazilian and the reference data sets together as test and training sets, higher R2 values were obtained, showing that in the union of the two databases, a good predictive model is obtained.

Numerical Modeling of the Dynamic Elastic Modulus of Concrete.

This article introduces simulations of theoretical material with controlled properties for the evaluation of the effect of key parameters, as volumetric fractions, elastic properties of each phase and transition zone on the effective dynamic elastic modulus. The accuracy level of classical homogenization models was checked regarding the prediction of dynamic elastic modulus. Numerical simulations were performed with the finite element method for evaluations of the natural frequencies and their correlation with Ed through frequency equations. An acoustic test validated the numerical results and obtained the elastic modulus of concretes and mortars at 0.3, 0.5 and 0.7 water-cement ratios. Hirsch calibrated according to the numerical simulation (x = 0.27) exhibited a realistic behavior for concretes of w/c = 0.3 and 0.5, with a 5% error. However, when the water-to-cement ratio (w/c) was set to 0.7, Young's modulus displayed a resemblance to the Reuss model, akin to the simulated theoretical triphasic materials, considering matrix, coarse aggregate and a transition zone. Hashin-Shtrikman bounds is not perfectly applied to theoretical biphasic materials under dynamic situations.

Use of operational research techniques for concrete mix design: A systematic review.

Traditional methods for designing concrete mixtures provide good results; however, they do not guarantee the optimum composition. Consequently, applying operational research techniques is motivated by an increasing need for designers to proportion the concrete's raw materials that satisfy the concrete performance requirements such as mechanical properties, chemical properties, workability, sustainability, and cost. For this reason, many authors have been looking for mathematical programming and machine learning solutions to predict concrete mix properties and optimise concrete mixtures. Therefore, a comprehensive review of operational research techniques concerning the design and proportioning of concrete mixtures and a classification framework are presented herein.

Atmospheric deterioration of ceramic building materials and future trends in the field: a review.

Multiple techniques have been developed and implemented around the world to monitor structures and minimize the costs of repairing, maintaining, and losing ceramic building materials due to environmental factors. Understanding the different degradation phenomena that affect ceramic building materials and evaluating their condition can help reduce material losses caused by deterioration and the need for interventions. This study reviews the main forms of atmospheric degradation that affect ceramic materials and the commonly employed methods to evaluate their deterioration. The aim is to illustrate the different types of atmospheric deterioration that affect ceramic materials and to demonstrate the current monitoring methods and testing. In addition to a literature review, a bibliometric analysis was conducted to highlight the available tools to counter atmospheric deterioration. The analysis shows that CO2, sulfates, and temperature are the most important types of degradation for ceramic construction materials. It was also discovered that due to their porous nature, ceramic construction materials require careful control as contaminants and water can easily penetrate them. The two most severe types of deterioration identified in this analysis for reinforced concrete were chloride-induced corrosion and carbonation.