Reliability Based Design Optimization of Concrete Mix Proportions Using Generalized Ridge Regression Model

This paper presents Reliability Based Design Optimization (RBDO) model to deal with uncertainties involved in concrete mix design process. The optimization problem is formulated in such a way that probabilistic concrete mix input parameters showing random characteristics are determined by minimizing the cost of concrete subjected to concrete compressive strength constraint for a given target reliability. Linear and quadratic models based on Ordinary Least Square Regression (OLSR), Traditional Ridge Regression (TRR) and Generalized Ridge Regression (GRR) techniques have been explored to select the best model to explicitly represent compressive strength of concrete. The RBDO model is solved by Sequential Optimization and Reliability Assessment (SORA) method using fully quadratic GRR model. Optimization results for a wide range of target compressive strength and reliability levels of 0.90, 0.95 and 0.99 have been reported. Also, safety factor based Deterministic Design Optimization (DDO) designs for each case are obtained. It has been observed that deterministic optimal designs are cost effective but proposed RBDO model gives improved design performance

A Statistical Approach to Optimizing Concrete Mixture Design

A step-by-step statistical approach is proposed to obtain optimum proportioning of concrete mixtures using the data obtained through a statistically planned experimental program. The utility of the proposed approach for optimizing the design of concrete mixture is illustrated considering a typical case in which trial mixtures were considered according to a full factorial experiment design involving three factors and their three levels (33). A total of 27 concrete mixtures with three replicates (81 specimens) were considered by varying the levels of key factors affecting compressive strength of concrete, namely, water/cementitious materials ratio (0.38, 0.43, and 0.48), cementitious materials content (350, 375, and 400 kg/m3), and fine/total aggregate ratio (0.35, 0.40, and 0.45). The experimental data were utilized to carry out analysis of variance (ANOVA) and to develop a polynomial regression model for compressive strength in terms of the three design factors considered in this study. The developed statistical model was used to show how optimization of concrete mixtures can be carried out with different possible options

Aplicación de la Inteligencia Artificial en el diseño de mezclas de concreto. Estado del Arte

En este artículo se presentan los diferentes esfuerzos en la aplicación de la técnicas de la Inteligencia Artificial para predecir algunas propiedades del concreto, tanto en estado fresco como endurecido. El Estado del Arte muestra un uso importante de las Redes Neuronales Artificiales y de los Algoritmos Evolutivos, que son aplicados en la predicción de propiedades, la optimización, la dosificación de la materia prima, el control de calidad y la validación de modelos. Finalmente, se revisan los avances para diseños de mezclas en concretos reforzados con fibras.Palabras clave: Algoritmos Evolutivos, Concreto reforzado con fibras, Diseño de mezclas de concreto, Inteligencia Artificial, Redes Neuronales Artificiales

Optimization of Porous Pavement Mixtures Based on Aggregate Structure

Porous pavements are sustainable features that are used to help manage the quantity and quality of stormwater runoff. These pavements may include porous asphalt, permeable interlocking concrete pavers and pervious concrete. Since pavements that are purposefully designed to drain water through their matrix are relatively new, contractors and engineers are faced with various challenges such as improper design and installation, poor workability, and excessive finishing which may lead to clogged pores. Therefore, this study on porous pavements examined pervious concrete mixtures to evaluate an optimization process for the preparation of porous pavement mixtures based on aggregate structure to meet desired performance criteria. Pervious concrete mixtures typically consist of aggregate, cement, water, little to no fines and admixtures. Since aggregate makes up a large portion of the pervious concrete mix, aggregate properties and proportioning were the main focus of this study. Two aggregate sources (L and C) were used in the preparation of pervious concrete mixtures. From these sources, three single-sized aggregate fractions were used in making blends, the #8 (2.36 mm), the #4 (4.75 mm) and the in. (9.5 mm). Aggregate properties such as uniformity coefficient were calculated and others were measured including specific gravity, absorption, density (dry rodded and dry Proctor), void content, percent flat and elongated, shape and surface texture (particle index), California Bearing Ratio penetration stress, and compaction indices. From source L, fifteen (15) sample groups of twelve (12) 6 in. × 6 in. cylindrical specimens were made and from source C, fourteen (14) sample groups were made similar to source L. The fresh pervious concrete had a water-cement ratio of 0.25, with a cement-aggregate ratio of 0.23 for source L and 0.25 for source C, and the unit weights (ASTM C1688 and an alternative method) and gravimetric air content were determined. Each sample group was divided into 4 subgroups of three specimens that had permeability values that were not statistically different from each other. Other tests conducted on the different subgroups included effective porosity, compressive strength, split tensile strength, and abrasion loss. The aggregate test results showed that source L, had higher specific gravities, percent absorption, and densities than source C, but lower void contents, percent flat and elongated, particle index, and California Bearing Ratio penetration stress at 0.2 inches. The approach taken in evaluating an optimization process was to use regression analysis in combination with the simplex-centroid design of the three aggregate sizes. Relationships were analyzed within and across aggregate properties and pervious concrete properties. The augmented simplex-centroid design with the polynomial special quartic model was used to predict the aggregate proportions that best fit the desired aggregate property or pervious concrete property. This design of experiment tool is a triangle with an elevated response surface on which contour lines present the predicted parameter values. For this study, the simplex triangle consisted of ten design points representing the aggregate proportions associated with the predicted parameters. The design points were located at the vertices, at the halfway point along the edges, and at the centroid, and three additional points within the triangle around the centroid on imaginary lines that run perpendicularly from the midpoint of an axis to the opposite vertex. The lack-of-fit test with α = 0.01 was used to check the adequacy of the model based on all the data points and also on only the validation points. Based on the lack-of-tests, the special quartic model was over 50% adequate for source L mixtures and over 80% adequate for source C. The optimization process included two options: Option 1 - A regression analysis is done to predict an aggregate property that relates well to a pervious concrete property. The contour line on the simplex response surface that represents the predicted aggregate property is then used to predict aggregate proportions that meet the desired aggregate property. Option 2 - The contour line for the desired pervious concrete property could be located on the simplex response surface and used to predict the aggregate proportions that meet the desired pervious concrete property

Exploración con redes neuronales artificiales para estimar la resistencia a la compresión, en concretos fibroreforzados con acero

By designing and building concrete structures, the compressive strength achieved at 28-day curing typically represents the stability control specification of any work. Furthermore, reinforcing fibers into the cement based matrix has allowed a gain to their properties, as well as a high performance material. Technical literature states predictive formulations of compressive strength of concrete in function of a few composition parameters, such as water/cement ratio and the Portland cement. Also, there are formulations to find the proportion of the raw materials to get a defined compressive strength, specifically non-reinforced ordinary concrete. Besides artificial neural networks as a metaphor of biological neurons have been used as a tool to predict concrete compressive strength. The experience in this application shows an increasing interest to develop applications using fiber-reinforced concrete. In this paper, an artificial neural network has been developed to predict the compressive strength of steel-fiber-reinforced-concrete. The results prove that developed artificial neural networks may perform an adequate approximation to the actual value of the mechanical property.En diseño y construcción de estructuras de concreto, la resistencia a la compresión a 28 días de curado es la especificación de control de estabilidad de la obra. La inclusión de fibras como reforzamiento de la matriz cementicia permite una ganancia en sus propiedades, además de obtener un material de alto desempeño. En las normativas, se plantean formulaciones predictivas de la resistencia a la compresión basadas en unos pocos parámetros de composición del concreto, tales como la relación agua/cemento y el contenido de cemento Portland. Por otra parte, también se han planteado métodos de diseños de concreto para definir la ponderación de sus materiales componentes, teniendo como referencia la resistencia a la compresión del concreto simple. Además, las redes neuronales artificiales, como un símil de las neuronas biológicas, han sido utilizadas como herramientas de predicción de la resistencia a la compresión en el concreto, también con referencia al concreto simple, sin reforzamiento con fibras. Los antecedentes en este uso muestran que es interesante desarrollar aplicaciones en los concretos reforzados con fibras. En el presente trabajo se elaboraron redes neuronales artificiales para predecir la resistencia a la compresión en concretos reforzados con fibras de acero. Los resultados de los indicadores de desempeño mostraron que las redes neuronales artificiales elaboradas pueden realizar una aproximación adecuada al valor real de la propiedad mecánica

A Comparison of Machine Learning Methods for Predicting the Compressive Strength of Field-Placed Concrete

This study evaluates the efficacy of machine learning (ML) methods to predict the compressive strength of field-placed concrete. We employ both field- and laboratory-obtained data to train and test ML models of increasing complexity to determine the best-performing model specific to field-placed concrete. The ability of ML models trained on laboratory data to predict the compressive strength of field-placed concrete is evaluated and compared to those models trained exclusively on field-acquired data. Results substantiate that the random forest ML model trained on field-acquired data exhibits the best performance for predicting the compressive strength of field-placed concrete; the RMSE, MAE, and R2 values were 730 psi, 530 psi, and 0.51, respectively. We also show that hybridization of field- and laboratory-acquired data for training ML models is a promising method for reducing common over-prediction issues encountered by laboratory-trained models that are used in isolation to predict the compressive strength of field-placed concrete.</p

Characterization of the acoustic properties of cementitious materials

The primary aim of this research was to investigate the fundamental acoustic properties of several cementitious materials, the influence of mix design parameters/constituents, and finally the effect of the physical and mechanical properties of cementitious material concrete/mortar on the acoustic properties of the material. The main objectives were: To understand the mechanism of sound production in musical instruments and the effects of the material(s) employed on the sound generated; To build upon previous research regarding selection of the tested physical/mechanical properties and acoustic properties of cementitious materials; To draw conclusions regarding the effect of different constituents, mix designs and material properties upon the acoustic properties of the material; To build a model of the relationship between the acoustic properties of a cementitious material and its mix design via its physical/mechanical properties. In order to meet the aim, this research was conducted by employing the semi-experimental (half analytical) method: two experimental programmes were performed (I and II); a mathematical optimization technique (least square method) was then implemented in order to construct an optimized mathematical model to match with the experimental data. In Experimental Programme I, six constituents/factors were investigated regarding the effect on the physical/mechanical and acoustic properties: cementitious material additives (fly ash, silica fume, and GGBS), superplasticizer, and basic mix design parameters (w/c ratio, and sand grading). 11 properties (eight physical/mechanical properties: compressive strength, density, hardness, flexural strength, flexural modulus, elastic modulus, dynamic modulus and slump test; and three acoustic properties: resonant frequency, speed of sound and quality factor (internal damping)) were tested for each constituents/factors related mortar type. For each type of mortar, there were three cubes, three prisms and three cylinders produced. In Experimental Programme I, 20 mix designs were investigated, 180 specimens produced, and 660 test results recorded. After analysing the results of Experimental Programme I, fly ash (FA), w/b ratio and b/s ratio were selected as the cementitious material/factors which had the greatest influence on the acoustic properties of the material; these were subsequently investigated in detail in Experimental Programme II. In Experimental Programme II, various combinations of FA replacement level, w/b ratios and b/s ratios (three factors) resulted in 1122 test results. The relationship between these three factors on the selected 11 properties was then determined. Through using regression analysis and optimization technique (least square method), the relationship between the physical/mechanical properties and acoustic properties was then determined. Through both experimental programmes, 54 mix designs were investigated in total, with 486 specimens produced and tested, and 1782 test results recorded. Finally, based upon well-known existing relationships (including, model of compressive strength and elastic modulus, and the model of elastic modulus and dynamic modulus), and new regressioned models of FA-mortar (the relationship of compressive strength and constituents, which is unique for different mixes), the optimized object function of acoustic properties (speed of sound and damping ratio) and mix design (proportions of constituents) were constructed via the physical/mechanical properties

Toward salt-scaling resistant concrete

The main objective of this research study was to (1) improve the understanding of the underlying mechanisms of durability-related deterioration in pavements; (2) improve the understanding of methods for extending pavement life, i.e., mixture ingredients; and (3) implement tools and specifications that will increase the longevity of concrete pavements. To this end, since it was of special interest to enhance current knowledge regarding salt-scaling resistance of concrete, an extensive effort was made in conducting a comprehensive literature review on the topic, after which an experimental program was designed to study the (i) relationship between the air-void system and salt scaling, (ii) effect of mixture components on hardened concrete properties and salt-scaling potential, (iii) impact of workmanship, i.e., effects of different finishing times and curing regimes on the scaling resistance of the concrete specimens, and (iv) correlation between concretes’ hardened properties and salt-scaling resistance. Statistical univariate and multivariable regression models were developed for use by researchers and field engineers, using non-destructive tests, i.e., ultrasonic pulse velocity (UPV) and rebound hammer (RH), to facilitate the prediction of concretes’ hardened properties and salt-scaling resistance. Ultimately, the contributions of each of the investigated factors on concretes’ hardened properties and salt scaling resistance were statistically investigated and corresponding multivariate-regression models were developed. The collection of mixture variables included water-to-cementitious materials (w/cm) ratio, paste volume, slag cement, and air content. Concrete performance was mainly investigated through tests of abrasion resistance, sorptivity, compressive strength, and salt scaling. Shrinkage and freeze-thaw resistance of the concrete mixtures were also tested to evaluate the effects of paste volume on concrete’s hardened properties. Finally, research continues toward assessing the correlation between cement chemistry and salt-scaling damage

Workability and strength attributes of RCC: Effects of different chemical admixtures and resulting paste

Roller compacted concrete (RCC) is a drier consistency concrete proportioned with dense graded aggregates and has lesser paste content. Primarily owing to its drier consistency, the fresh and mechanical performances of RCC are distinctly different from conventional concretes. The objectives of this work were three fold. The first objective was to explain the anomalous behavior of fresh RCC in terms of different components of workability. The second objective was to explain the mechanisms of strength development in terms of paste quality and quantity. The final objective was to explain the roles of different chemical admixtures (water reducers, retarders, rheology modifiers, air entraining agents, dry cast products, etc.) in influencing the workability and strength performances of RCC. It is observed that the relative volume and quality of the paste, in addition to combined aggregate grading, affect the overall workability of mixtures. The workability of concrete is characterized in terms of cohesion, angle of internal friction, air content, compactibility, and consistency retention over time. Air content plays a decisive role in influencing the performances of concrete. The resulting mobility of the paste influences the compactibility, which in-turn decides the strength as well. RCC shows anomalous behavior in terms of mechanical strength as shown by deviations in the Abrams\u27 law. It is argued that water-binder ratio is not a comprehensive parameter to explain the overall concrete behavior and trends. A significant body of knowledge is added in terms of the use of chemical admixtures in RCC. Atypical behaviors in influencing the fresh and hardened properties are explained by offering plausible mechanisms in terms of binder-admixture interactions. Irrespective of the admixture type, higher than normal dosages are required for RCC

Aplicación de procesos gaussianos para mejorar la precisión en la estimación de la resistencia a la compresión del concreto reforzado

RESUMEN En el presente trabajo de investigación se desarrolló un método basado en procesos gaussianos para mejorar la precisión en la estimación de la resistencia a la compresión del concreto reforzado. Para lograr esto, se hizo uso de una base de datos de 1030 registros, que contiene las cantidades de los componentes de la mezcla de hormigón y la resistencia a la compresión que alcanzó la estructura a lo largo del tiempo (en días), estos datos fueron donados y están disponibles en el repositorio de datos de aprendizaje automático. Estos datos fueron utilizados en el análisis descriptivo, en donde se comprobó a través de un análisis correlacional que la relación matemática que existe entre los componentes del hormigón y la resistencia a la compresión, es no lineal. El desarrollo del método consistió en la elaboración de algoritmos en lenguaje de programación de Python, dentro del cual se utilizó la librería de procesos gaussianos. Dentro de los procesos gaussianos se desarrolló nuevas funciones de covarianza. Una vez establecido el proceso gaussiano se llevó a cabo el proceso de optimización que consistió en la obtención de los parámetros de las nuevas funciones de covarianza cuyos valores minimizan la raíz del error cuadrático medio. La obtención de los parámetros trajo como resultado la obtención de modelos de regresión ( o modelos predictivos), siendo 12 el total de modelos obtenidos de los cuales a través de un análisis de resultados, considerando el valor obtenido correspondiente a los indicadores de la raíz del error cuadrático medio y el logaritmo de la probabilidad marginal, se seleccionó cuatro modelos como los más óptimos y precisos para llevar a cabo estimaciones de la resistencia a la compresión del concreto reforzado. La precisión con la que estiman los modelos obtenidos, compite con otros modelos que utilizan otro tipo de algoritmos como máquina de soporte vectorial o redes neuronales artificiales, los cuales fueron descritos en la revisión de la literatura, mientras que las comparaciones hechas con modelos desarrollados en base procesos gaussianos de la revisión de la literatura, demuestra la superioridad de los modelos obtenidos en la presente investigación