Exploring the Effect of In-plane Tensile Forces on the Two-way Shear Strength: review, comparative study and future works

Two-way shear failure of slabs is a sudden one, which has catastrophic outcomes. Slabs with large spans may be subjected to in-plane tensile forces due to thermal or earthquake loading. There is a lack of agreement between various design codes regarding the significance of in-plane tensile forces on the two-way shear strength. Two-way shear failure of slabs is a sudden one, which has catastrophic outcomes. Slabs with large dimensions may be subjected to in-plane tensile forces due to restraint or earthquake loading. There is a lack of agreement between various design codes regarding the significance of in-plane tensile forces on the two-way shear strength. The purpose of this study is to explore, propose a simplified two-way shear strength model, which includes the effect of in-plane tensile forces on the strength. A review for the experimental investigations, existing models, design codes for two-way shear of slabs is presented, with emphasis on in-plane tensile forces. The loading method used in the current experimental testing is misleading, where the two-way shear and the in-plane forces are independent. A comparative study was conducted between the existing formula and design codes for this case. The comparison between different codes with the experimental results show that the new proposed Eurocode design code was found to be the most accurate one. However, it did not include the effect of the in-plane tensile forces in a physically sound manner. In addition, more full testing of concrete slabs under combined two-way shear and tensile forces are required to refine this existing two-way shear design code provisions or develop new formulas or mechanical models

Experimental and Numerical Studies on Flexural Behavior of GGBS-Based Geopolymer Ferrocement Beams

The ferrocement structural concept has been shown to offer exceptional mechanical properties in terms of toughness, fracture control, and impact resistance, which are achieved by tight spacing and homogeneous reinforcement dispersion within the matrix. The flexure behavior of geopolymer ferrocement beams under axial flexural stress is being explored experimentally and computationally in this present work. Under flexural loads, nine samples of geopolymer ferrocement beams 150 mm thick, 75 mm wide, and 1700 mm long were tested to failure. The reinforcing steel bars and wire meshes, as well as the quantity of wire mesh layers, were the key factors studied. The initial crack load, ultimate failure load, and mid-span deflection with various loading phases, cracking patterns, energy absorption, and ductility index were all studied in relation to the behavior. In terms of carrying capacity, absorbing energy, and ductility, welded steel wire mesh beams fared better than other materials. Using ANSYS-19 software, nonlinear finite element analysis (NLFEA) was carried out to demonstrate the behavior of composite ferrocement geopolymer beams. The ensuing experimental and numerical data demonstrated that the degree of experimental value estimation supplied by the FE simulations was sufficient. It is crucial to demonstrate that, in comparison to control specimens, the increase in strength of specimens reinforced with tensar meshes was reduced by around 15%. Doi: 10.28991/CEJ-2023-09-03-010 Full Text: PD

Shear Performance of GFRP Reinforced Concrete Beams with Seawater and Chopped Fiber

This paper reports an experimental study on the behavior and shear strength of concrete beams reinforced with longitudinal GFRP bars mixed with sea water. In order to evaluate how much concrete contributes to shear resistance, seven beams were tested in bending. Similar in size and concrete strength, the beams were longitudinally reinforced with glass fiber-reinforced polymer bars; however, they did not even have shear reinforcement. The beams, which measured 3,100 mm in length, 400 mm in depth, and 200 mm in width, were conducted and tested up to failure. The test variables were longitudinal reinforcement ratios (1.0, 1.4, and 2.0%), chopped fiber content (0, 0.5, 2, and 3 kg/m3), and mixing water type (freshwater and seawater). The test findings showed that increasing the reinforcement ratio increased the neutral-axis depth and allowed the formation of more closely spaced fractures while decreasing the loss of flexural stiffness after cracking. By increasing the area of concrete in compression, this in turn enhances the contribution of aggregate interlock as well as the contribution of uncracked concrete. Furthermore, increasing the reinforcement ratio improves the dowel action, which reduces the tensile stresses that are created in the concrete around it. Doi: 10.28991/CEJ-2023-09-04-05 Full Text: PD

Modified Artificial Neural Networks and Support Vector Regression to Predict Lateral Pressure Exerted by Fresh Concrete on Formwork

In this study, a modifed Artifcial Neural Network (ANN) and Support Vector Regression (SVR) with three diferent optimization algorithms (Genetic, Salp Swarm and Grasshopper) were used to establish an accurate and easy-to-use module to predict the lateral pressure exerted by fresh concrete on formwork based on three main inputs, namely mix proportions (cement content, w/c, coarse aggregates, fne aggregates and admixture agent), casting rate, and height of specimens. The data have been obtained from 30 previously piloted experimental studies (resulted 113 samples). Achieved results for the model including all the input data provide the most excellent prediction of the exerted lateral pressure. Additionally, having diferent magnitudes of powder volume, aggregate volume and fuid content in the mix exposes diferent rising and descending in the lateral pressure outcomes. The results indicate that each model has its own advantages and disadvantages; however, the root mean square error values of the SVR models are lower than that of the ANN model. Additionally, the proposed models have been validated and all of them can accurately predict the lateral pressure of fresh concrete on the panel of the formwork

Utilization of Bitumen Modified with Pet Bottles as an Alternative Binder for the Production of Paving Blocks

This study considers the utilization of bitumen modified with molten polyethylene terephthalate (PET) waste bottles as an alternative binder in paving blocks. PET waste was used at 2, 4, 6, 8, and 10% to modify bitumen in the production of paving blocks. Compressive strength test and skid resistance test were conducted on the paving block samples to evaluate their mechanical strength properties, while water absorption and the Cantabro abrasion tests were carried out to ascertain the durability of the paving block samples. The PET-modified bitumen paving blocks (PMBPB) have enhanced compressive strength and skid resistance compared to unmodified bitumen paving blocks. Also, a significant reduction in water absorption rate of up to 56% was achieved in PET-modified bitumen paving blocks (PMBPB) compared to the unmodified sample. The abrasion loss in the PMBCB samples was the least compared to that in normal cement paving blocks and unmodified bitumen paving blocks. The maximum compressive strength and least water absorption for the PET-modified bitumen concrete paving blocks were obtained at a 10% PET replacement level. It can be concluded that enhanced compressive strength and durability in cement paving blocks and unmodified bitumen paving blocks could be achieved with the use of PET modified bitumen in concrete paving block production, and this will also encourage PET waste recycling and contribute meaningfully to sustainability in concrete paving block production. Doi: 10.28991/CEJ-2023-09-01-08 Full Text: PD

Optimizing the Flexural Behavior of Bamboo Reinforced Concrete Beams Containing Cassava Peel Ash using Response Surface Methodology

The growing concern to reduce global warming has necessitated the use of more eco-friendly materials in construction. The study is focused on the utilization of cassava peel ash as supplementary cementitious material and bamboo as reinforcement in concrete beams. The response surface methodology approach was explored to determine the effect of simultaneously varying the cassava peel ash content, bamboo size, beam length, and beam depth on the flexural strength and strain of beams. An analysis of variance was carried out on experimentally obtained results to determine the accuracy of the obtained models and the contributions made by the linear interaction and quadratic terms on flexural strength and flexural strain. The coefficient of determination obtained for RSM models showed a good correlation between all predicted and experimentally obtained results. The optimum conditions obtained for bamboo-reinforced concrete containing cassava peel ash were 3% cassava peel ash, 16 mm bamboo diameter, 500 mm beam length, and 150 mm beam depth. The predicted flexural strengths were 11.85, 14.34, and 14.95 N/mm2 and flexural strains of 0.64, 0.67, and 0.91 for 28 days, 56 days, and 90 days, respectively. To validate the model prediction, a laboratory experiment was conducted using the optimum mix design proportion. From the results obtained, it was observed that the experimental results were close to those predicted by the models. These models can be efficiently used for simulating the flexural behavior of bamboo-reinforced concrete beams. Doi: 10.28991/CEJ-2023-09-08-011 Full Text: PD

Application of machine learning algorithms to evaluate the influence of various parameters on the flexural strength of ultra-high-performance concrete

The effect of various parameters on the flexural strength (FS) of ultra-high-performance concrete (UHPC) is an intricate mechanism due to the involvement of several inter-dependent raw ingredients. In this digital era, novel artificial intelligence (AI) approaches, especially machine learning (ML) techniques, are gaining popularity for predicting the properties of concrete composites due to their better precision than typical regression models. In addition, the developed ML models in the literature for FS of UHPC are minimal, with limited input parameters. Hence, this research aims to predict the FS of UHPC considering extensive input parameters (21) and evaluate each their effect on its strength by applying advanced ML approaches. Consequently, this paper involves the application of ML approaches, i.e., Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Gradient Boosting (GB), to predict the FS of UHPC. The GB approach is more effective in predicting the FS of UHPC precisely than the SVM and MLP algorithms, as evident from the outcomes of the current study. The ensembled GB model determination coefficient (R2) is 0.91, higher than individual SVM with 0.75 and individual MLP with 0.71. Moreover, the precision of applied models is validated by employing the k-fold cross-validation technique. The validity of algorithms is ensured by statistical means, i.e., mean absolute error and root mean square errors. The exploration of input parameters (raw materials) impact on FS of UHPC is also made with the help of SHAP analysis. It is revealed from the SHAP analysis that the steel fiber content feature has the highest influence on the FS of UHPC

Exploring the antecedents of AI adoption for effective HRM practices in the Indian pharmaceutical sector

Purpose: The aim of this research is to investigate the factors that facilitate the adoption of artificial intelligence (AI) in order to establish effective human resource management (HRM) practices within the Indian pharmaceutical sector.Design/methodology/approach: A model explaining the antecedents of AI adoption for building effective HRM practices in the Indian pharmaceutical sector is proposed in this study. The proposed model is based on task-technology fit theory. To test the model, a two-step procedure, known as partial least squares structural equational modeling (PLS-SEM), was used. To collect data, 160 HRM employees from pharmacy firms from pan India were approached. Only senior and specialized HRM positions were sought.Findings: An examination of the relevant literature reveals factors such as how prepared an organization is, how people perceive the benefits, and how technological readiness influences AI adoption. As a result, HR systems may become more efficient. The PLS-SEM data support all the mediation hypothesized by proving both full and partial mediation, demonstrating the accuracy of the proposed model.Originality: There has been little prior research on the topic; this study adds a great deal to our understanding of what motivates human resource departments to adopt AI in the pharmaceutical companies of India. Furthermore, AI-related recommendations are made available to HRM based on the results of a statistical analysis