Ocean energy in Indian coasts and islands for sustainability-A roadmap for future

Dauji, Saha Techno-Press 2017 Advances in energy research Vol.5 No.4

Limited quantity and non-uniform distribution of fossil fuel over the world, along with the environmental concerns of increasing $CO_2$ emissions, indicate that gradual and planned switchover to the sustainable energy sources is the need of the day. Ocean energy is well-distributed over the coasts, abundant, renewable and available in the form of wave energy, tidal energy and thermal energy. India has gathered precious experience from the pilot plants utilizing these methods over the last few years. One of the main constraints is deemed to be the grid connectivity. Time has come to transform this limitation into opportunity. Ocean power can be a very suitable option for the coastal belts and the islands. Implementation of this concept would require large-scale industry participation along with favourable government policies in the coming years. This article attempts a review of the ocean energy initiatives in India and proposes a roadmap for the future.

Prediction of concrete spall damage under blast: Neural approach with synthetic data

Saha Dauji 사단법인 한국계산역학회 2020 Computers and Concrete, An International Journal Vol.26 No.6

The prediction of spall response of reinforced concrete members like columns and slabs have been attempted by earlier researchers with analytical solutions, as well as with empirical models developed from data generated from physical or numerical experiments, with different degrees of success. In this article, compared to the empirical models, more versatile and accurate models are developed based on model-free approach of artificial neural network (ANN). Synthetic data extracted from the results of numerical experiments from literature have been utilized for the purpose of training and testing of the ANN models. For two concrete members, namely, slabs and columns, different sets of ANN models were developed, each of which proved to have definite advantages over the corresponding empirical model reported in literature. In case of slabs, for all three categories of spall, the ANN model results were superior to the empirical models as evaluated by the various performance metrics, such as correlation, root mean square error, mean absolute error, maximum overestimation and maximum underestimation. The ANN models for each category of column spall could handle three variables together: namely, depth, spacing of longitudinal and transverse reinforcement, as contrasted to the empirical models that handled one variable at a time, and at the same time yielded comparable performance. The application of the ANN models for spall prediction of concrete slabs and columns developed in this study has been discussed along with their limitations.

Bond strength of corroded reinforcement in concrete: Neural and tree based approaches

Dauji, Saha Techno-Press 2021 Structural monitoring and maintenance Vol.8 No.3

Reinforcement corrosion affects the existing concrete structures, particularly in the coastal regions. One of the effects of corrosion of reinforcement is degradation of the bond stress that can be developed between the reinforcement and the surrounding concrete and this in turn affects the capacity of the reinforced concrete member. Prediction of the bond stress applicable for the corroded reinforcement has been attempted using analytical, empirical and soft computing methods. This article presents the comparative performance of two data-driven tools, artificial neural network (ANN) and decision tree (DT) for the task of prediction of bond stress from the corrosion level, the compressive strength of concrete and the ratio of cover and diameter of reinforcement bar. From the extensive evaluation of performance with both quantitative and graphical methods, it was concluded that the ANN approach would be better suited for the application, with the available data. For development of the models 8-fold cross validation scheme was adopted due to the limitations of data. The ANN models trained with pull-out test data, when employed with ensemble approach in predictive mode for a different experiment setup and bond strength test (flexural) data, could produce results comparable to ANN models trained with flexural test data (reported in literature). The inclusion of the additional factors (compressive strength of concrete and the ratio of cover and diameter of reinforcement bar), 8-fold cross validation approach, and ensemble prediction could be the possible reasons for achieving such portability of pull-out test based model for prediction of flexural test data.

Axial capacity of FRP reinforced concrete columns: Empirical, neural and tree based methods

Saha Dauji 국제구조공학회 2024 Structural Engineering and Mechanics, An Int'l Jou Vol.89 No.3

Machine learning (ML) models based on artificial neural network (ANN) and decision tree (DT) were developed for estimation of axial capacity of concrete columns reinforced with fiber reinforced polymer (FRP) bars. Between the design codes, the Canadian code provides better formulation compared to the Australian or American code. For empirical models based on elastic modulus of FRP, Hadhood et al. (2017) model performed best. Whereas for empirical models based on tensile strength of FRP, as well as all empirical models, Raza et al. (2021) was adjudged superior. However, compared to the empirical models, all ML models exhibited superior performance according to all five performance metrics considered. The performance of ANN and DT models were comparable in general. Under the present setup, inclusion of the transverse reinforcement information did not improve the accuracy of estimation with either ANN or DT. With selective use of inputs, and a much simpler ANN architecture (4-3-1) compared to that reported in literature (Raza et al. 2020: 6-11-11-1), marginal improvement in correlation could be achieved. The metrics for the best model from the study was a correlation of 0.94, absolute errors between 420 kN to 530 kN, and the range being 0.39 to 0.51 for relative errors. Though much superior performance could be obtained using ANN/DT models over empirical models, further work towards improving accuracy of the estimation is indicated before design of FRP reinforced concrete columns using ML may be considered for design codes.

Estimation of Present-Day Strength of Concrete for a 40-year-old Building from Non-destructive Tests: A Case Study

Soubhagya Karmakar,Dauji Saha,Kshirsagar Sandeep Shankar,Satish Kumar Saini,Bhargava Kapilesh,Mahapatra Kamalendu 아시아콘크리트학회 2021 Journal of Asian Concrete Federation Vol.7 No.2

Assessment of the present health of existing concrete structures is necessary, particularly for enhancing the life of the infrastructure facilities reaching the end of their design life. The codes stipulate establishment of sitespecific correlation expressions to estimate the compressive strength of concrete from indirect non-destructive tests (NDT) such as rebound hammer or ultrasonic pulse velocity tests. However, in certain circumstances, requisite number of partially destructive (core) tests required for establishing the site-specific equations might not be feasible. In such scenario, selection of a suitable correlation expression from literature has to be performed in a rational way, as discussed in this article with a case study of a 40-year-old concrete building. From the study, it has been observed that for the limited number of direct tests, the Indian code stipulation resulted in higher characteristic strength of concrete as compared to the parametric estimation, which can be attributed to the assumption of Normal distribution and code stipulated (conservative) standard deviation value. In case of the indirect estimation cases, the parametric characteristic strength was pretty close to the corresponding non-parametric values indicating that the fitted distributions represented the strength values very well. Recommendations for the suitable correlation expression from literature applicable for estimation of equivalent strength from NDT for the structure, recommendation for characteristic compressive strength of concrete and the suggestions for accounting for the inaccuracies in estimated strength in subsequent structural re-analysis have been provided from the results of the study.

Effect of clay as deleterious material on properties of normal-strength concrete

Harish R. Choudhary,Saha Dauji,Arham Siddiqui 아시아콘크리트학회 2020 Journal of Asian Concrete Federation Vol.6 No.1

Sustainability concerns prompted use of crushed aggregates in concrete, wherein deleterious materials might get inadvertently included. Some deleterious materials are allowed up to limiting values by most standards, which, however, are silent about the quantification of their effects on properties of concrete – which would be site specific. For an important Indian infrastructure, this study quantifies effects of clay fines as deleterious material in concrete, on workability (slump) and strength (cube compression; split tensile; flexural tensile tests) around the limit (1% of fine aggregates by weight) stipulated by the Indian standard. The novelty of the study is that, contrary to the literature in this domain, the effects are studied without alteration of the mix proportions – a different practical scenario. The limit of clay fines in concrete allowed by Indian standard was found to be adequate considering strength parameters, but for maintaining target workability, the limit would be revised to 0.5% of the fine aggregates. Generally, the variations of concrete properties with the increasing clay fines were: (1) the workability and split tensile strength reduced monotonically, in non-linear fashion; (2) compressive strength (beyond 7 days) and the flexural tensile strength (modulus of rupture) reduced monotonically in linear manner.