Effect of medium-density fiberboard wastes ash on calcium silicate hydrate crystal of concrete.

Due to the high consumption of Medium-density fiberboard (MDF), waste products of this material are growing worldwide. In this research, the feasibility of using Medium-density fiberboard waste ash (MDFWA) as part of cement in concrete was investigated. For this purpose, 0, 5, 10, 15, 20, and 25% of the cement in concrete was substituted with MDFWA. For all design mixes, the water/blind ratio and the volume of aggregates were same. The slump, compressive strengths, SEM, EDX, TGA, DSC, and FTIR tests were conducted on the samples. At 28 days, the results demonstrated that the compressive strength of the sample containing 20% MDFWA increased by 13.6% compared to the control sample. Furthermore, the microstructure of the concrete show that the voids of the sample containing 20% MDFWA reduced compared to the control sample and also more calcium silicate hydrate (C-S-H) crystal formed.Implications: The significance of the present paper is to solve the environmental issue caused by large amount of Medium-density fiberboard waste ash (MDFWA) and produce also sustainable concrete. In addition, the replacement of cement with MDFWA increases the compressive strength and enhancement of the microstructure of concrete due to extra C-S-H products. Therefore, the findings confirm that by using 20% MDFWA, a more eco-friendly production, denser, sustainable, economical, and stronger concrete would be achieved.

Soft computing techniques to predict the compressive strength of green self-compacting concrete incorporating recycled plastic aggregates and industrial waste ashes.

Abstract: Rapid urbanization and industrialization with corresponding economic growth have increased concrete production, leading to resource depletion and environmental pollution. The mentioned problems can be resolved by using recycled aggregates and industrial waste ashes as natural aggregate and cement replacement in concrete production. Incorporating different by-product ashes and recycled plastic (RP) aggregates are viable options to produce sustainable self-compacting concrete (SCC). On the other hand, compressive strength is an essential characteristic among other evaluated properties. As a result, establishing trustworthy models to forecast the compressive strength of SCC is critical to saving cost, time, and energy. Furthermore, it provides valuable instruction for planning building projects and determining the best time to remove the formwork. In this study, four alternative models were suggested to predict the compressive strength of SCC mixes produced by RP aggregates: the artificial neural network (ANN), nonlinear model, linear relationship model, and multi-logistic model. To do so, an extensive set of data consisting of 400 mixtures were extracted and analyzed to develop the models, various mixture proportions and curing times were considered as input variables. To test the effectiveness of the suggested models, several statistical evaluations, including coefficient of determination (R 2), scatter index, root mean squared error (RMSE), mean absolute error (MAE), and Objective (OBJ) value were utilized. Compared to other models, the ANN model performed better to forecast the compressive strength of SCC mixes incorporating RP aggregates. The RMSE, MAE, OBJ, and R 2 values for this model were 5.46 MPa, 2.31 MPa, 4.26 MPa, and 0.973, respectively.

Modeling the compressive strength of eco-friendly self-compacting concrete incorporating ground granulated blast furnace slag using soft computing techniques.

Concern regarding global climate change and its detrimental effects on society demands the building sector, one of the major contributors to global warming. Reducing cement usage is a significant challenge for the concrete industry; achieving this objective can help reduce global carbon dioxide emissions. Replacing the cement in concrete with by-product ashes is a promising approach for reducing the embodied carbon in concrete and improving some of its properties. Among different by-product ashes, ground granulated blast furnace slag (GGBFS) is a viable option to produce sustainable self-compacting concrete (SCC). Compressive strength (CS), on the other hand, is an essential characteristic among other evaluated properties. As a result, establishing trustworthy models to forecast the CS of SCC is critical to saving cost, time, and energy. Furthermore, it provides helpful instruction for planning building projects and determining the best time to remove the formwork. In this study, four alternative models were suggested to predict the CS of SCC mixes produced by GGBFS: the artificial neural network (ANN), nonlinear model (NLR), linear relationship model (LR), and multi-logistic model (MLR). To do so, an extensive set of data consisting of about 200 mixtures were extracted and analyzed to develop the models, and various mixture proportions and curing times were considered input variables. To test the effectiveness of the suggested models, several statistical evaluations including determination coefficient (R2), mean absolute error (MAE), scatter index (SI), root mean squared error (RMSE), and objective (OBJ) value were utilized. In comparison to other models, the ANN model performed better to forecast the CS of SCC mixes incorporating GGBFS. The RMSE, MAE, OBJ, and R2 values for this model were 4.73 MPa, 2.3 MPa, 3.4 MPa, and 0.955, respectively.

Systematic multiscale models to predict the compressive strength of fly ash-based geopolymer concrete at various mixture proportions and curing regimes.

Geopolymer concrete is an inorganic concrete that uses industrial or agro by-product ashes as the main binder instead of ordinary Portland cement; this leads to the geopolymer concrete being an eco-efficient and environmentally friendly construction material. A variety of ashes used as the binder in geopolymer concrete such as fly ash, ground granulated blast furnace slag, rice husk ash, metakaolin ash, and Palm oil fuel ash, fly ash was commonly consumed to prepare geopolymer concrete composites. The most important mechanical property for all types of concrete composites, including geopolymer concrete, is the compressive strength. However, in the structural design and construction field, the compressive strength of the concrete at 28 days is essential. Therefore, achieving an authoritative model for predicting the compressive strength of geopolymer concrete is necessary regarding saving time, energy, and cost-effectiveness. It gives guidance regarding scheduling the construction process and removal of formworks. In this study, Linear (LR), Non-Linear (NLR), and Multi-logistic (MLR) regression models were used to develop the predictive models for estimating the compressive strength of fly ash-based geopolymer concrete (FA-GPC). In this regard, a comprehensive dataset consists of 510 samples were collected in several academic research studies and analyzed to develop the models. In the modeling process, for the first time, twelve effective variable parameters on the compressive strength of the FA-GPC, including SiO2/Al2O3 (Si/Al) of fly ash binder, alkaline liquid to binder ratio (l/b), fly ash (FA) content, fine aggregate (F) content, coarse aggregate (C) content, sodium hydroxide (SH)content, sodium silicate (SS) content, (SS/SH), molarity (M), curing temperature (T), curing duration inside ovens (CD) and specimen ages (A) were considered as the modeling input parameters. Various statistical assessments such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), OBJ value, and the Coefficient of determination (R2) were used to evaluate the efficiency of the developed models. The results indicated that the NLR model performed better for predicting the compressive strength of FA-GPC mixtures compared to the other models. Moreover, the sensitivity analysis demonstrated that the curing temperature, alkaline liquid to binder ratio, and sodium silicate content are the most affecting parameter for estimating the compressive strength of the FA-GPC.

Development of eco-efficient lightweight self-compacting concrete with high volume of recycled EPS waste materials.

Reusing the industrial waste materials is one of the main aims of sustainability and achieve the environmental protection. However, concrete is the main production for recycling waste materials and cleaning the climate. The utilization of self-consolidating lightweight concrete (SCLC) can achieve two important advantages of the structure self-weight reduction and improving workability. This paper examined the effect of waste expanded polystyrene (EPS) beads on the workability and hardened characteristics of sustainable SCLCs. Six different EPS volume fractions up to 80% replaced with normal coarse aggregate to produce SCLC mixtures with water to binder (w/b) ratio of 0.35. A total binder content of 500 kg/m3 by including 20% waste ceramic powder with 80% Portland cement and fine aggregate consist of river sand and fine ceramic with 1:1 ratio in all SCLC mixes. The workability of SCLCs was examined by slump flow time and diameter, L-box height ratio, V-funnel flow time, and segregation resistance. Moreover, the hardened properties tested at different curing periods such as compressive strength at 7, 28, and 90 days; flexural strength at 28 and 90 days; and splitting tensile strength, dry density, voids percent, water absorption, ultrasonic pulse velocity (UPV); and scanning electron microscope (SEM) at 28 days. The results verified that workability of SCLCs enhanced as EPS incorporation increased and achieved the limitations required for self-compacting concrete (SCC) while the strengths value curtailed but the compressive strength satisfied the lower value indicated by ACI for structural purposes. Depending on the water absorption and UPV, results illustrated that all produced sustainable SCLC mixtures had a good durability. Furthermore, a high linear correlation was noticed between the results.

Mechanical and Fracture Parameters of Ultra-High Performance Fiber Reinforcement Concrete Cured via Steam and Water: Optimization of Binder Content.

An investigational study is conducted to examine the effects of different amounts of binders and curing methods on the mechanical behavior and ductility of Ultra-High Performance Fiber Reinforced Concretes (UHPFRCs) that contain 2% of Micro Steel Fiber (MSF). The aim is to find an optimum binder content for the UHPFRC mixes. The same water-to-binder ratio (w/b) of 0.12 was used for both water curing (WC) and steam curing (SC). Based on the curing methods, two series of eight mixes of UHPFRCs containing different binder contents ranging from 850 to 1200 kg/m3 with an increment of 50 kg/m3 were produced. Mechanical properties such as compressive strength, splitting tensile strength, static elastic module, flexural tensile strength and the ductility behavior were investigated. This study revealed that the mixture of 1150 kg/m3 binder content exhibited the highest values of the experimental results such as a compressive strength greater than 190 MPa, a splitting tensile strength greater than 12.5 MPa, and a modulus of elasticity higher than 45 GPa. The results also show that all of the improvements began to slightly decrease at 1200 kg/m3 of the binder content. On the other hand, it was concluded that SC resulted in higher mechanical performance and ductility behavior than WC.

The Effect of Incorporating Silica Stone Waste on the Mechanical Properties of Sustainable Concretes.

Incorporating various industrial waste materials into concrete has recently gained attention for sustainable construction. This paper, for the first time, studies the effects of silica stone waste (SSW) powder on concrete. The cement of concrete was replaced with 5, 10, 15, and 20% of the SSW powder. The mechanical properties of concrete, such as compressive and tensile strength, were studied. Furthermore, the microstructure of concrete was studied by scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy analysis (EDX), Fourier transformed infrared spectroscopy (FTIR), and X-Ray diffraction (XRD) tests. Compressive and tensile strength of samples with 5% SSW powder was improved up to 18.8% and 10.46%, respectively. As can be observed in the SEM images, a reduced number of pores and higher density in the matrix can explain the better compressive strength of samples with 5% SSW powder.