Background:This study investigated the development of engineering students' approaches to learning and views on collaboration in a PBL environment.Material and methods:An explanatory mixed research approach was employed with participants from four PBL-implementing engineering courses in Qatar and China. 197students responded to two surveys, and 168 students participated in group interviews.Results:While the study reveals increased adoption of deep approaches to learning on team projects, little influence on surface approaches to learning was found. The study also provides evidence supporting the positive relationship between students' adoption of deep learning approaches and their acknowledgement of values of collaboration in teamwork.Conclusions:This study suggests that while PBL characteristics may support deep learning, certain factors may underpin surface learning, including a feeling of insecurity during first experiences with it, lack of skills, and assessment methods that favor surface learning. Further efforts on engaging students with PBL may benefit both deep learning and team effectiveness.
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This qualitative longitudinal study explored three engineering instructors' professional agency in implementing project-based learning (PBL), including multiple sets of data (18 interviews, observations, and written reflections spanning three semesters). The results show that the instructors' care for students, interest in pedagogical innovation, and efforts in professional learning supported their confidence in stance, sense of agency, and competence in PBL practice. The efficacy and strong beliefs of the instructors further supported their agency-in-action regarding how they acted to influence their teaching by negotiating both its content and conditions. The study also revealed how individual resources and social conditions frame the enactment of professional agency and how the instructors developed agency and strategies to overcome challenging issues at work. Although they developed different types of agency, they demonstrated the same attitude towards resilience and negotiation for autonomy as well as the same engagement in self-empowerment and transforming their agentic competence into educational leadership.
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This study focuses on the numerical and statistical analyses to predict the mid-span moment capacity of RC beams strengthened with fabric reinforcement cementitious mortar (FRCM) laminate. A finite element model (FEM) has been built to simulate twelve RC beams strengthened with two types of FRCM, namely Polyparaphenylene benzobisoxazole (PBO) FRCM and Carbon (C) FRCM. The FE models were verified based on experimental work available in the literature. The finite element models have shown a good agreement with experimental results in terms of maximum load-carrying capacity, load-deflection curves, and concrete strain values. The numerical simulation was followed by a parametric study on 42 models using face centred response surface methodology (RSM). Combining FEM and RSM, a novel mathematical model has been proposed to predict the mid-span moment capacity of the RC beams strengthened with FRCM. The results of the proposed model have shown optimal predictability with R2 equal to 90.34%. In addition, the proposed model agreed with the ACI design procedures and the existing literature.
Exiting literature revealed that fabric reinforced cementitious matrix (FRCM) is a promising material for the strengthening of shear deficient reinforced concrete (RC) beams. However, most of the available experimental studies are devoted to the use of FRCM system for the strengthening of slender beams and limited literature is available on the strengthening of deep beams using FRCM system. Moreover, there is no available literature on the analytical modelling of FRCM-strengthened deep beams. In this paper, a simple strut and tie model (STM) has been used to predict the ultimate load carrying capacity of RC deep beams strengthened in shear using FRCM system. The model accounts for the internal transverse reinforcement ratio and axial rigidity of the FRCM system. The proposed model is validated against an experimental results of RC deep beams strengthened with different types of FRCM system available in the literature.
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This study investigates the influence of basalt fiber on the rheological, mechanical, and microstructural properties of sustainable self-compacting concrete (SCC) incorporating fly ash and microsilica as supplementary cementitious materials (SCMs). Various SCC mixes were prepared, incorporating five different volume fractions of basalt fiber (0.05%, 0.1%, 0.5%, 1%, and 1.5%), along with a control mix. The rheological properties of fresh SCC were evaluated using slump flow and V-funnel flow tests. Subsequently, the mechanical properties, including compressive strength, splitting tensile strength, and flexural strength, were measured after 28 days of curing. Additionally, microstructural analysis was conducted using scanning electron microscopy (SEM) on fractured specimen surfaces. The results indicated that the inclusion of basalt fiber adversely affected the flowability of fresh SCC mixes, with increased fiber volume. However, the hardened concrete exhibited significant improvements in mechanical properties with the addition of basalt fibers. The optimal performance was observed in the SCC70-85/0.10 mix specimens, which demonstrated a 69.90% improvement in flexural strength and a 23.47% increase in splitting tensile strength compared with the control specimen. SEM analysis further revealed enhanced microstructural density in the concrete matrix containing basalt fiber. A two-factor analysis of variance (ANOVA) with repetitions was conducted to evaluate the effects of varying basalt fiber concentrations on the compressive, flexural, and tensile strengths of SCC mixes. The ANOVA results indicated significant effects for both SCC grade and basalt fiber concentration, demonstrating that each factor independently affected the compressive, tensile, and flexural strengths of SCC. These findings suggest that the incorporation of basalt fibers holds promise for extending building lifespans and enhancing concrete quality, representing a valuable advancement in structural engineering applications.
This paper presents a data-driven approach to determine the load and flexural capacities of reinforced concrete (RC) beams strengthened with fabric reinforced cementitious matrix (FRCM) composites in flexure. A total of seven machine learning (ML) models such as kernel ridge regression, K-nearest neighbors, support vector regression, classification and regression trees, random forest, gradient boosted trees, and extreme gradient boosting (xgBoost) are evaluated to propose the best predictive model for FRCM-strengthened beams. Beam geometry, internal steel reinforcement area, FRCM reinforcement area, and mechanical characteristics of concrete, steel, and FRCM are the main input parameters included in the database. Among the studied ML models, the xgBoost model is the most accurate model with the highest coefficient of determination (R2=99.3%) and least root mean square (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). A comparative study of the performance of the proposed and existing analytical models revealed the superior predictive capability and robustness of the proposed model. The predicted flexural and load capacities of the beams based on the existing analytical models are highly scattered and either over-conservative or unsafe. A unified SHapley Additive exPlanations approach is employed to explain the output of the best ML model and identify the most significant input features and interactions that influence the capacity of FRCM-strengthened RC beams in flexure. Furthermore, a reliability analysis is performed to calibrate the resistance reduction factor (ϕ) to achieve a specified target reliability index (βT=3.5).
Abstract The process of desalination results in the production of a hypersaline waste by-product known as reject brine. In some locations, this reject brine is dumped back into the ocean, which has potentially detrimental effects on water quality and marine life. This study was carried out to investigate whether this brine could potentially be used to manufacture cementitious materials. The effects of different concentrations of simulated reject brine on hydration kinetics, compressive strength, and drying shrinkage of cement paste and mortar were investigated. Cement paste and mortars were prepared using a water-to-cement ratio of 0.45 and were mixed with simulated reject brine, tap water, and diluted reject brine (an equal mass mixture of reject brine and tap water). The results show that the reject brine causes an acceleration of early cement hydration; however, this effect is negligible at later ages. Mixtures containing reject brine have higher compressive strength at early ages, although this difference is reduced at 91 d. The reject brine causes a drastic increase in the drying shrinkage. The difference between the results obtained using reject brine and diluted reject brine were generally insignificant, which suggests that the effects of solution composition on the observed properties were not strong when solution concentrations were greater than a threshold value. Although these results are preliminary and further feasibility studies, including research on concrete durability, are required, the results suggest that reject brine may be used to make unreinforced concrete or concrete reinforced with noncorrosive materials.
