Publications

Abstract To preserve the environment and natural resources, steel slag recovery conserves natural resources and makes landfill space available. Steel slag as a waste material has been partially substituted for fine (sand) and coarse aggregate in concrete (gravel). Compressive strength (CS) is the most significant mechanical attribute for all forms of concrete composites. To save time, energy, and money, it is essential to create accurate models for forecasting the CS of normal concrete (NC). In addition, it offers essential information for organizing the building work and details the ideal time to remove the formwork. In total, 338 data points were gathered, processed, and modeled in total. During the modeling approach, the most influential elements impacting the compressive strength (CS) of concrete with steel slag replacement were addressed. According to the modeling method, the most effective parameter which affects the compressive strength of normal concrete is the curing time. This research employed a Multi Logistic Regression model (MLR), an Artificial Neural Network (ANN), a Full Quadratic model (FQ), an M5P‐tree model, and an Interaction model to predict the compressive strength of normal strength concrete (CS ranged from 10 to 55 MPa) with steel slag aggregate replacement. According to data from the literature, the steel slag concentration enhanced the compressive strength. Based on evaluations with statistical tools like the objective (OBJ) function, the scatter index, and the Taylar diagram, the ANN model with the lowest root mean square error did better at predicting compressive strength than the other models.

The use of supplementary cementitious materials (e.g.fly ash and ground granulated blast furnace slag) as a partial replacement of ordinary Portland cement (OPC) in blended cement (BC) concrete has been established in recent years.However, an optimum mix design of BC concrete should not only achieve the targeted mechanical properties but also aim at minimizing the cost as well as environmental impacts.The first step towards an accurate estimate of BC concrete's environmental impacts is to define an indicative functional unit.Unfortunately, most of the research that uses life cycle assessment (LCA) to compare OPC and BC concrete, selects a simple functional unit without taking into consideration the difference in mechanical properties and durability characteristics.Although several efforts were found integrating the durability characteristics in the environmental impact assessment over the whole life cycle, no consensus was found.In this study, a novel approach to quantifying the environmental impact of BC concrete compared to OPC concrete as a part of a decision making process on the type and mixing proportions of concrete to minimize its environmental impact.Using LCA, the method proposes that performance based parameters are defined: a targeted compressive strength and service life.Predictions should be made of these parameters based on generic user-input data like type of BC, water/binder ratios, binder content and OPC replacement rates.The comparative environmental impact is then quantified accordingly.It is believed that this novel approach allows the user to accurately select and proportion a blended cement concrete mix with the objective of minimizing its environmental impact.

Abstract Self‐compacting concrete (SCC) is a specialized type of concrete that features excellent fresh properties, enabling it to flow uniformly and compact under its weight without vibration. SCC has been one of the most significant advancements in concrete technology over the past two decades. In efforts to reduce the environmental impact of cement production, a major source of CO 2 emissions, silica fume (SF) is often used as a partial replacement for cement. SF‐modified SCC has become a common choice in construction. This study explores the effectiveness of soft computing models in predicting the compressive strength (CS) of SCC modified with varying amounts of silica fume. To achieve this, a comprehensive database was compiled from previous experimental studies, containing 240 data points related to CS. The compressive strength values in the database range from 21.1 to 106.6 MPa. The database includes seven independent variables: cement content (359.0–600.0 kg/m 3 ), water‐to‐binder ratio (0.22–0.51), silica fume content (0.0–150.0 kg/m 3 ), fine aggregate content (680.0–1166.0 kg/m 3 ), coarse aggregate content (595.0–1000.0 kg/m 3 ), superplasticizer content (1.5–15.0 kg/m 3 ), and curing time (1–180 days). Four predictive models were developed based on this database: linear regression (LR), multi‐linear regression (MLR), full‐quadratic (FQ), and M5P‐tree models. The data were split, with two‐thirds used for training (160 data points) and one‐third for testing (80 data points). The performance of each model was evaluated using various statistical metrics, including the coefficient of determination ( R 2 ), root mean square error (RMSE), mean absolute error (MAE), objective value (OBJ), scatter index (SI), and a‐20 index. The results revealed that the M5P‐tree model was the most accurate and reliable in predicting the compressive strength of SF‐based SCC across a wide range of strength values. Additionally, sensitivity analysis indicated that curing time had the most significant impact on the mixture's properties.

Portland cement concrete is fragile in tension and it has numerous negative impacts on the environment. To deal with these issues, both fiber reinforcement and recycled materials can be utilized to manufacture sustainable and ductile concrete. In this study, the synergistic effects of high-performance mineral admixture silica fume and glass fiber reinforcement were investigated on the hardened properties of RC. For this purpose, two concrete mix families, namely, NC and RC were prepared. To understand the benefits of synergistic utilization of glass fiber and silica fume, in both NC and RC, 0.5% glass fiber was incorporated with three different levels of silica fume. i.e., 0%, 5%, and 10%. Both strength and permeability-related durability properties were investigated. Results revealed that combined incorporation of 0.5% fiber and 10% silica fume can help in the production of RC having better mechanical and durability performance compared to reference “NC”. Simultaneous incorporation of silica fume and glass fiber produces a combined effect greater than their individual effects on both mechanical and permeability properties of concrete. Silica fume plays a very dominant and positive role in the development of CS, WA, and CIPR of RC, whereas glass fiber plays a vital role in upgrading STS and FS of RC and whereas, with the addition of 0.5% glass fiber, RC can yield 8–9 times higher flexural toughness than that of the plain NC.

This study aims to establish systematic multiscale models to predict the compressive strength of cement mortar containing a high volume of fly ash (FA) and to be used by the construction industry with no theoretical restrictions. For that purpose, a wide experimental data (a total of 450 tested cement mortar modified with FA) from different academic research studies have been statically analyzed and modeled. For that purpose, Linear and Nonlinear regression, M5P-tree, and Artificial Neural Network (ANN) technical approaches were used for the qualifications. In the modeling process, most relevant parameters affecting the strength of cement mortar, i.e. fly ash (class C and F) incorporation ratio (0−70% of cement's mass), water-to-binder ratio (0.235–1.2), and curing ages (1–365 days). According to the correlation coefficient (R), mean absolute error and the root mean a square error, the compressive strength of cement mortar can be well predicted in terms of w/b, fly ash, and curing time using various simulation techniques. The results of this study suggest that the Non-linear regression-based model (NLR) and ANN are performing better than other applied models using training and testing datasets. The sensitivity investigation concludes that the curing time is the most dominating parameter for the prediction of the compressive strength of cement mortar with this data set.

Recycled aggregates (RAs) have been playing an important role in replacing natural aggregates (NAs) in concrete production, thereby contributing to a reduction in the extraction of natural resources and the promotion of a circular economy. However, it is important to assess the global impacts of this replacement, in both environmental and economic terms. In this study, an overview of the impacts of the production of natural and recycled aggregates is presented, using the life cycle assessment (LCA) methodology. Through this methodology, products with the same function are compared and information about the best solutions is given, considering their environmental and economic impacts. Studies with data collected from specific producers were compared, as well as environmental product declarations (EPDs) and generic databases, regarding the production of natural and recycled, coarse and fine, and rolled and crushed aggregates. This study intends therefore to provide the environmental and economic impact comparison at the global level through LCA from different data sources. According to this literature review, the best and worst environmental results are assigned to lower and higher transport distances, respectively. Regarding EPDs, the lowest environmental impacts are related to recycled coarse aggregates and the highest to natural coarse crushed aggregates. In terms of generic databases, the results are similar, with the lowest impacts associated with natural fine rolled aggregates and the highest to natural coarse crushed aggregates. In what concerns the economic impacts, in general, recycled aggregates are associated with the lowest costs. However, these results are highly dependent on transport distances and costs.

It is generally known that the two most crucial elements of concrete that depend on the slump value of the mixture are workability and compressive strength. In addition, slump retention is more delicate than the commonly used slump value since it reflects the concrete mixture’s durability for usage in civil engineering applications. In this study, the effect of three water-reducer additives was tested on concrete’s workability and compressive strength from 1 day to 28 days of curing. The slump of the concrete was measured at the time of adding water to the mix and after 30 min of adding water. This study employed 0–1.5% (%wt) water-reducer additives. The original ratio between water and cement (wc) was 0.65, 0.6, and 0.56 for mixtures incorporating 300, 350, and 400 kg of cement. It was lowered to 0.3 by adding water-reducer additives based on the additives type and cement content. Depending on the kind and amount of water-reducer additives, w/c, gravel content, sand content, crushed content, and curing age, adding water-reducer additives to the concrete increased its compressive strength by 8% to 186%. When polymers were added to the concrete, they formed a fiber net (netting) that reduced the space between the cement particles. As a result, joining the cement particles quickly enhanced the fresh concrete’s viscosity and the hardened concrete’s compressive strength. The study aims to establish mathematical models (nonlinear and M5P models) to predict the concrete compressive strength when containing water-reducer additives for construction projects without theoretical restrictions and investigate the impact of mix proportion on concrete compressive strength. A total of 483 concrete samples modified with 3 water-reducer additives were examined, evaluated, and modeled for this study.