Given the increasing global concern of freshwater scarcity, the use of seawater in concrete mixtures appears to be a way forward towards achieving sustainable concrete, especially in the case of non-reinforced concrete applications or with the use of non-corrosive reinforcement. This paper reports on the results of an experimental study to compare the freshwater-and seawater-mixed concretes in terms of their strength, shrinkage and permeability performance. The experimental program included the following: (i) compressive strength test (at 3, 7, 28, and 56-day ages); (ii) concrete shrinkage test (at Days 4, 7, 14, 21, 28, and 56 following mixing); and (iii) permeability tests (rapid chloride permeability and water absorption at Days 28 and 56 following mixing). As for the study results, seawater concrete showed a slightly higher early-age (i.e., till Day 7) strength performance than that of freshwater-mixed counterpart, followed by a strength performance that is 7–10% inferior to the freshwater concrete after 28 days or later. Also, the shrinkage of seawater concrete was slightly higher than that of freshwater concrete, with a difference of 5% reported after 56 days following mixing. Finally, the permeability performance of hardened concrete in seawater and freshwater mixtures was similar.
Recently, seawater has emerged as viable mixing water for concrete, especially in the case of non-reinforced concrete applications or with the use of non-corrosive reinforcement. Previous studies concerning seawater-mixed concrete mostly revealed an initial slight increase in its strength performance (i.e., till Day 14 following mixing), followed by a strength reduction of 7–15% (i.e., after 28 days or longer) as compared to the conventional freshwater-mixed concrete. With an attempt to explain such observations, this paper aims at comparing the microstructure of freshwater-and seawater-mixed cement pastes. Scanning electron microscopy was utilized to observe the microstructure of freshwater and seawater pastes at Days 3 and 28 following mixing. At Day 3, seawater paste was observed to have more densified microstructure as compared to that of the freshwater counterpart, resulting in relatively higher strength performance. At Day 28, the microstructure was almost similar for the two cement pastes. However, seawater paste was observed to have salt impurities as a result of seawater ions, which possibly cause a slightly lower strength performance as compared to the freshwater paste.
Fiber-reinforced polymer (FRP) composites have recently been considered in the field of structural engineering as one of the best alternatives to conventional steel reinforcement due to their high tensile strength, lightweight, cost-effectiveness, and superior corrosion resistance. However, the variation in FRP physical and mechanical characteristics among the different FRP types and manufacturers makes it difficult to predict the strength of FRP-reinforced concrete (RC) members. For that reason, an efficient prediction tool was developed for a fast, accurate, and intelligent (FAI) prediction of the flexural capacity of FRP-RC beams based on the result of an optimized super-learner machine learning (ML) model. A database of the experimental results on the flexural strength of FRP-RC beams was compiled and randomly split into 80% train and 20% test sets. Six factors were considered in the model; namely, width and effective depth of the beam, concrete compressive strength, FRP flexural reinforcement ratio, FRP modulus of elasticity, and FRP ultimate tensile strength. Grid search is combined with a 10-fold cross-validation to optimize the hyperparameters of the ML models. The prediction capability of the proposed super-learner ML model was benchmarked against boosting- and tree-based ML models, such as classification and regression trees, adaptive boosting, gradient boosted decision trees, and extreme gradient boosting. Moreover, a comparison with the existing code and guideline equations showed that the proposed super-learner ML model provided the most desirable prediction of the flexural capacity of FRP-RC beams.
First Name is required invalid characters Last Name is required invalid characters Email Address is required Invalid Email Address Invalid Email Address
The application of inorganic composites has proven to be an effective strengthening technique for shear-critical reinforced concrete (RC) beams. However, accurate prediction of the shear capacity of RC beams strengthened with inorganic composites has been a challenging problem due to its complex failure mechanism and the interaction between the internal and external shear reinforcements. Besides, the predictive capabilities of the existing models are not satisfactory. Thus, this research proposed machine learning (ML) based models for predicting the shear capacity of RC beams strengthened in shear with inorganic composites, for the first time, considering all important variables. The results of the analyses evidenced that the proposed ML models can be successfully used to predict the shear capacity of shear-critical RC beams strengthened with inorganic composites. Among the ML models examined herein, the extreme gradient boosting (xgBoost) model showed the highest prediction capability. The comparison among the predictions of the proposed xgBoost and existing models evidenced that the efficacy of the xgBoost model is superior to the existing models in terms of accuracy, safety, and economic aspects. Finally, reliability analysis is performed to calibrate the resistance reduction factors in order to attain target reliability indices of 3.5 and 4.0 for the proposed model.
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.
First Name is required invalid characters Last Name is required invalid characters Email Address is required Invalid Email Address Invalid Email Address
Textile reinforced mortar (TRM) is applied on the concrete surface with the aim of strengthening reinforced concrete structures. The performance of the strengthened structural system is directly related to the bond between the existing concrete substrate and the freshly applied TRM layer. This paper presents the results of an experimental study carried out to investigate the significance of concrete surface preparation, performed prior to strengthening, on the bonding behavior of the TRM system. For this purpose, concrete slabs of size (500 mm × 500 mm × 100 mm) were prepared and strengthened using a 10-mm thick TRM layer. After that, the bond performance of the strengthening layer with the concrete slab was assessed using the pull-off test. Three different levels of surface roughening were considered before strengthening: (i) no roughening (regarded as the reference), (ii) low roughening level, and (iii) high roughening level. Two types of textile materials are used in strengthening systems: carbon and polyparaphenylene benzobisoxazole (PBO). A total number of 72 pull-off tests were performed, of which the results were analyzed to examine the significance of the test variables. Results revealed that as the concrete surface is more roughened before strengthening, the bond between concrete substrate and TRM layer becomes stronger. Moreover, the PBO-TRM systems exhibit more desirable bonding behavior compared to the carbon-TRM counterpart.
Predictions show that more than half of the world population will lack sufficient freshwater by 2025. Yet, the construction industry uses a considerable amount of freshwater to produce concrete. To save resources of fresh water, using seawater seems to be a valid potential alternative that can replace freshwater for mixing concrete. This paper presents a short review performed on existing literature related to the usage of seawater in concrete structures. As a summary of the work presented: (a) It is noticeable that the current literature, generally, reports little or no negative effect of seawater on the characteristics of plain concrete, both in the short and in the long term; (b) steel corrosion caused by the presence of chloride appears to be the sole reason for not accepting the use of seawater in concrete preparation; (c) Fiber reinforced polymer (FRP) is discussed as a promising alternative to steel for seawater-concrete reinforcement, owing to their light weight, high tensile strength, and adequate corrosion resistance; and (d) A future outlook for using seawater accompanied by FRP reinforcement in concrete structures is discussed in terms of achieving sustainability goals.
First Name is required invalid characters Last Name is required invalid characters Email Address is required Invalid Email Address Invalid Email Address
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.
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.
