i-manager Publications

Volume 13 Issue 2 April - June 2023

Research Paper

Creep of Concrete Incorporated with Marble Powder

Dr Syed Afzal Basha* , B. Jayarami Reddy**

* G Pullaiah College of Engineering and Technology, Kurnool, Andhra Pradesh, India.

** Rajiv Gandhi University of Knowledge Technologies, Ongole Campus, Andhra Pradesh, India.

Basha, S. A., and Reddy, B. J. (2023). Creep of Concrete Incorporated with Marble Powder. i-manager's Journal on Civil Engineering, 13(2), 1-13. https://doi.org/10.26634/jce.13.2.19834

Abstract

This study presents an experimental investigation of the mechanical properties of concrete incorporating Kadapa Marble Powder (KMP) as an additional cementitious material. It focuses on varying the content of KMP (ranging from 0% to 15% by weight of cement) and conducting tests on compressive strength, modulus of elasticity, and creep. The replacement of cement with KMP led to improvements in the compressive strength and modulus of elasticity, and the incorporation of KMP contributed to a reduction in the creep phenomenon of concrete over time. The beneficial effects of KMP can be attributed to its ability to react with calcium hydroxide generated during cement hydration. This reaction results in the production of additional calcium silicate hydrate, which fills voids and large pores within the concrete matrix. As a result, the porosity associated with the capillary pores and voids decreased. This observation was supported by the examination of the microstructure of hardened concrete using scanning electron microscope techniques. The presence of KMP enhanced cement hydration and contributed to a reduction in the porosity associated with the gel pores. This was attributed to the release of absorbed water retained in the small pores of the KMP particles. This study highlighted the potential of incorporating KMP as a beneficial cementitious material for concrete production. These findings suggest that KMP can enhance the mechanical properties of concrete, including the compressive strength and modulus of elasticity, while mitigating the creep phenomenon. The analysis also provides insights into the microstructural changes that occur in concrete due to the inclusion of the KMP.

References

[1]. Alyamaç, K. E., & Aydin, A. B. (2015). Concrete properties containing fine aggregate marble powder. KSCE Journal of Civil Engineering, 19(7), 2208-2216.

[2]. Alyamaç, K. E., & İnce, R. (2007). Study on the usability of waste marble mud in self-compacting concrete as a powder material. In Proceedings of TÇMB Third International Symposium (pp. 821-832).

[3]. Asamoto, S., Kato, K., & Maki, T. (2014). Effect of creep induction at an early age on subsequent prestress loss and structural response of prestressed concrete beam. Construction and Building Materials, 70, 158-164.

[4]. Aydın, A. B. (2013). The Effect of the Marble Powder Used as Fine Material on the Durability of Concrete (Doctoral dissertation, Firat University, Elazig).

[5]. Bazant, Z. P. (1975). Theory of creep and shrinkage in concrete structures: A precis of recent developments. Mechanics Today, 2(1), 1-93.

[6]. Brooks, J. J., & Johari, M. M. (2001). Effect of metakaolin on creep and shrinkage of concrete. Cement and Concrete Composites, 23(6), 495-502.

[7]. Chen, J., Yuan, Y., Zhu, Q., & Duan, J. (2023). Hightemperature resistance of high-strength concrete with iron tailing sand. Journal of Building Engineering, 63, 105544.

[8]. Chen, L., Fang, Q., Jiang, X., Ruan, Z., & Hong, J. (2015). Combined effects of high temperature and high strain rate on normal weight concrete. International Journal of Impact Engineering, 86, 40-56.

[9]. Corinaldesi, V., Moriconi, G., & Naik, T. R. (2010). Characterization of marble powder for its use in mortar and concrete. Construction and Building Materials, 24(1), 113-117.

[10]. de Sensale, G. R., Ribeiro, A. B., & Gonçalves, A. (2008). Effects of RHA on autogenous shrinkage of Portland cement pastes. Cement and Concrete Composites, 30(10), 892-897.

[11]. Demirel, B. (2010). The effect of the using waste marble dust as fine sand on the mechanical properties of the concrete. International Journal of the Physical Sciences, 5(9), 1372-1380.

[12]. Drouet, E., Poyet, S., & Torrenti, J. M. (2015). Temperature influence on water transport in hardened cement pastes. Cement and Concrete Research, 76, 37-50.

[13]. Fu, D., Xia, C., Xu, S., Zhang, C., & Jia, X. (2022). Effect of concrete composition on drying shrinkage behavior of ultra-high performance concrete. Journal of Building Engineering, 62, 105333.

[14]. Fu, W., Sun, B., Noguchi, T., Zhao, W., & Ye, J. (2023). Prediction of internal relative humidity of concrete under different thermal conditions using an enhanced long short-term memory network. Thermal Science and Engineering Progress, 38, 101629.

[15]. Giorla, A. B., & Dunant, C. F. (2018). Microstructural effects in the simulation of creep of concrete. Cement and Concrete Research, 105, 44-53.

[16]. Hamada, H., Alattar, A., Tayeh, B., Yahaya, F., & Almeshal, I. (2022). Influence of different curing methods on the compressive strength of ultra-high-performance concrete: A comprehensive review. Case Studies in Construction Materials, e01390.

[17]. He, Z. H., Li, L. Y., & Du, S. G. (2017). Creep analysis of concrete containing rice husk ash. Cement and Concrete Composites, 80, 190-199.

[18]. Hong, S. H., Choi, J. S., Yuan, T. F., & Yoon, Y. S. (2022). A review on concrete creep characteristics and its evaluation on high-strength lightweight concrete. Journal of Materials Research and Technology, 22, 230-251.

[19]. Jiang, Z., Sun, Z., & Wang, P. (2006). Internal relative humidity distribution in high-performance cement paste due to moisture diffusion and self-desiccation. Cement and Concrete Research, 36(2), 320-325.

[20]. Khatri, R. P., Sirivivatnanon, V., & Gross, W. (1995). Effect of different supplementary cementitious materials on mechanical properties of high performance concrete. Cement and Concrete Research, 25(1), 209-220.

[21]. Kong, X., Fang, Q., Chen, L., & Wu, H. (2018). A new material model for concrete subjected to intense dynamic loadings. International Journal of Impact Engineering, 120, 60-78.

[22]. Li, H., Li, Y., Jin, C., Liu, J., Liu, Y., & Mu, J. (2022). Meso-scale modelling of the effect of coarse aggregate properties on the creep of concrete. Journal of Building Engineering, 54, 104660.

[23]. Li, J., & Yao, Y. (2001). A study on creep and drying shrinkage of high performance concrete. Cement and Concrete Research, 31(8), 1203-1206.

[24]. Martínez-Barrera, G., & Brostow, W. (2010). Effect of marble particle size and gamma irradiation on mechanical properties of polymer concrete. e-Polymers, 10(1), 61.

[25]. Minfei, L., Yidong, G., Ze, C., Zhi, W., Erik, S., & Branko, Š. (2022). Microstructure-informed deep convolutional neural network for predicting short-term creep modulus of cement paste. Cement and Concrete Research, 152, 106681.

[26]. Nguyen, H. A., Chang, T. P., Chen, C. T., & Huang, T. Y. (2022). Engineering and creep performances of green super-sulfated cement concretes using circulating fluidized bed combustion fly ash. Construction and Building Materials, 346, 128274.

[27]. Pihlajavaara, S. E. (1974). A review of some of the main results of a research on the ageing phenomena of concrete: Effect of moisture conditions on strength, shrinkage and creep of mature concrete. Cement and Concrete Research, 4(5), 761-771.

[28]. Ranaivomanana, N., Multon, S., & Turatsinze, A. (2013). Tensile, compressive and flexural basic creep of concrete at different stress levels. Cement and Concrete Research, 52, 1-10.

[29]. Rossi, P., Tailhan, J. L., & Le Maou, F. (2013). Creep strain versus residual strain of a concrete loaded under various levels of compressive stress. Cement and Concrete Research, 51, 32-37.

[30]. Sadek, D. M., El-Attar, M. M., & Ali, H. A. (2016). Reusing of marble and granite powders in selfcompacting concrete for sustainable development. Journal of Cleaner Production, 121, 19-32.

[31]. Santos, S. B., da Silva Filho, L. C. P., & Calmon, J. L. (2012). Early-age creep of mass concrete: Effects of chemical and mineral admixtures. ACI Materials Journal, 109(5), 537-544.

[32]. Shariq, M., Prasad, J., & Abbas, H. (2016). Creep and drying shrinkage of concrete containing GGBFS. Cement and Concrete Composites, 68, 35-45.

[33]. Tawfik, M. E., & Eskander, S. B. (2006). Polymer concrete from marble wastes and recycled poly (ethylene terephthalate). Journal of Elastomers & Plastics, 38(1), 65-79.

[34]. Uysal, M., & Sumer, M. (2011). Performance of selfcompacting concrete containing different mineral admixtures. Construction and Building Materials, 25(11), 4112-4120.

[35]. Wei, X., & Ren, X. (2023). Coupled viscosity-damage model for concrete under high strain rate. Engineering Fracture Mechanics, 277, 108985.

[36]. Wyrzykowski, M., & Lura, P. (2014). The effect of external load on internal relative humidity in concrete. Cement and Concrete Research, 65, 58-63.

[37]. Xupeng, C., Zhuowen, S., & Jianyong, P. (2021). Study on metakaolin impact on concrete performance of resisting complex ions corrosion. Frontiers in Materials, 8, 788079.

[38]. Yu, P., Duan, Y. H., Fan, Q. X., & Tang, S. W. (2020). Improved MPS model for concrete creep under variable humidity and temperature. Construction and Building Materials, 243, 118183.

[39]. Yu, P., Li, R. Q., Bie, D. P., Yao, X. M., Liu, X. C., & Duan, Y. H. (2022). A coupled creep and damage model of concrete considering rate effect. Journal of Building Engineering, 45, 103621.

[40]. Zhao, Q., Liu, X., & Jiang, J. (2015). Effect of curing temperature on creep behavior of fly ash concrete. Construction and Building Materials, 96, 326-333.

Full Article (HTML)

Pdf

Research Paper

Evaluation of Probability Distributions for Estimation of Peak Flood Discharge using FFA Approach

Vivekanandan N.*

Central Water and Power Research Station, Pune, Maharashtra, India.

Vivekanandan, N. (2023). Evaluation of Probability Distributions for Estimation of Peak Flood Discharge using FFA Approach. i-manager's Journal on Civil Engineering, 13(2), 14-20. https://doi.org/10.26634/jce.13.2.19947

Abstract

The estimation of Peak Flood Discharge (PFD) for a specific return period is of paramount importance in hydrological studies for the planning, design, and management of civil and hydraulic structures. This research investigates the evaluation of probability distributions through statistical tests, viz., the Chi-Square, Kolmogorov-Smirnov, and D-index. These tests aid in estimating the PFD using the Flood Frequency Analysis (FFA) approach for the Tapi River at Prakasha Barrage. In this analysis, various distributions from the extreme value family such as Extreme Value Type-1, Extreme Value Type-2, Generalized Extreme Value (GEV), and Pareto, were employed in the FFA. The distribution parameters are determined by standard techniques, such as the method of moments, Maximum Likelihood Method (MLM), and the Method of L-Moments (LMO). A qualitative assessment, along with the D-index value, was employed to select the most suitable distribution for the analyzed data. This study demonstrates that among the four distributions adopted in FFA, the GEV (LMO) distribution exhibits the best fit. Furthermore, this research suggests the use of PFDs derived from the GEV (LMO) distribution for design purposes.

References

[1]. Bezak, N., Brilly, M., & Šraj, M. (2014). Comparison between the peaks-over-threshold method and the annual maximum method for flood frequency analysis. Hydrological Sciences Journal, 59(5), 959-977.

[2]. Bhat, M. S., Alam, A., Ahmad, B., Kotlia, B. S., Farooq, H., Taloor, A. K., & Ahmad, S. (2019). Flood frequency analysis of river Jhelum in Kashmir basin. Quaternary International, 507, 288-294.

[3]. Koyejo, S. O., Akomolafe, A. A., Awogbemi, C. A., & Oladimeji, A. O. (2020). Extension of comparative analysis of estimation methods for Frechet distribution parameters. International Journal of Research and Innovation in Applied Science, 5(4), 58-75.

[4]. Lima, C.H., Lall, U., Troy, T., & Devineni, N. (2016). A hierarchical Bayesian GEV model for improving local and regional flood quantile estimates. Journal of Hydrology, 541, 816- 823.

[5]. Murphy, P. J. (2001). Evaluation of mixed-population flood-frequency analysis. Journal of Hydrologic Engineering, 6(1), 62-70.

[6]. Mukherjee, M.K. (2013). Flood frequency analysis of River Subernarekha, India, using Gumbel's extreme value distribution. International Journal of Computational Engineering Research, 3(7), 12–19.

[7]. Pawar, U., & Hire, P. (2018). Flood frequency analysis of the Mahi Basin by using Log Pearson Type III probability distribution. Hydrospatial Analysis, 2(2), 102–112.

[8]. Rao, A.R., & Hamed, K.H. (2000). Flood Frequency Analysis. CRC Press, Boca Raton, Florida, USA.

[9]. Sabarish, R.M., Narasimhan, R., Chandhru, A. R., Suribabu, C. R., Sudharsan, J., & Nithiyanantham, S. (2015). Probability analysis for consecutive-day maximum rainfall for Tiruchirapalli City (South India, Asia). Applied Water Science, 7, 1033-1042.

[10]. Sahoo, B. B., Jha, R., Singh, A., & Kumar, D. (2020). Bivariate low flow return period analysis in the Mahanadi River basin, India using copula. International Journal of River Basin Management, 18(1), 107–116.

[11]. Sasireka, K., Suribabu, C. R., & Neelakantan, T.R. (2019). Extreme rainfall return periods using Gumbel and Gamma distributions. International Journal of Recent Technology and Engineering, 8(4), 27-29.

[12]. Subyani, A.M. (2011). Hydrologic behavior and flood probability for selected arid basins in Makkah area,Western Saudi Arabia. Arabian Journal of Geosciences, 4(5– 6), 817–824.

[13]. Tanaka, T., Tachikawa, Y., Ichikawa, Y., & Yorozu, K. (2017). Impact assessment of upstream flooding on extreme flood frequency analysis by incorporating a flood- inundation model for flood risk assessment. Journal of Hydrology, 554, 370–382.

[14]. USWRC. (1975). Guidelines for Determining Flood Flow Frequency (No. 17). US Water Resources Council, Hydrology Committee.

[15]. Vivekanandan, N., & Srishailam, C. (2020). Selection of best fit probability distribution for extreme value analysis of rainfall. CBIP Water and Energy International, 63(10), 13-19.

[16]. Zhang, J. (2002). Powerful goodness-of-fit tests based on the likelihood ratio. Journal of the Royal Statistical Society Series B: Statistical Methodology, 64(2), 281-294.

Full Article (HTML)

Pdf

Research Paper

Experimental Investigation on Concrete by Partial Replacement of Fine Aggregate with Ceramic Powder

Ruthrapriya P.* , R. Hariprabam, P. Ragunathapandian*

*-*** Department of Structural Engineering, Arulmigu Meenakshi Amman College of Engineering, Vadamavandal, Thiruvannamalai District, Tamilnadu, India.

Ruthrapriya, P., Hariprabam, R., and Ragunathapandian, P. (2023). Experimental Investigation on Concrete by Partial Replacement of Fine Aggregate with Ceramic Powder. i-manager's Journal on Civil Engineering, 13(2), 21-29. https://doi.org/10.26634/jce.13.2.19815

Abstract

An enormous amount of industrial waste is generated annually by India's manufacturing sector, and unless resources are efficiently conserved and managed, this problem will continue to worsen. This waste contains nearly a million tons of ceramic debris. India accounts for over 6% of global manufacturing, and currently ranks third in terms of consumption. Similarly, a significant amount of waste is generated after tile utilization, and its disposal poses a challenge. With extensive construction and infrastructure development, natural sand is rapidly being depleted from water sources. This research proposes a suitable replacement for fine aggregates by effectively utilizing ceramic waste powder in concrete, which can address the issues of fine aggregate replenishment and the disposal of broken ceramic tiles.

References

[1]. Aruna, D., Prabhu, R., Yaragal, S. C., & Venkataramana, K. (2015). Studies on usage potential of broken tiles as part replacement to coarse aggregates in concretes. International Journal of Research in Engineering and Technology, 4(7), 110-114.

[2]. Awoyera, P. O., Ndambuki, J. M., Akinmusuru, J. O., & Omole, D. O. (2018). Characterization of ceramic waste aggregate concrete. HBRC Journal, 14(3), 282-287.

[3]. Babria, J. P., Pitroda, J. R., & Vaghela, K. B. (2019). Effective use of ceramic waste powder as partial replacement of cement in establishing sustainable concrete. Journal of Emerging Technologies and Innovative Research, 6(4), 337-348.

[4]. Chand, G., & Kumar, P. R. (2017). Partial Replacement of Aggregate with Ceramic Tile in Concrete (Master Thesis, Department of Civil Engineering, Pace Institute of Technology and Sciences), pp. 1-48.

[5]. Correia, J. R., de Brito, J., & Pereira, A. S. (2006). Effects on concrete durability of using recycled ceramic aggregates. Materials and Structures, 39, 169-177.

[6]. Danish, M. F., Dabhekar, K., More, S. R., & Khedikar, I. P. (2020). Study of partial replacement of coarse aggregate by mosaic tile chips. International Research Journal of Engineering and Technology (IRJET), 7(5), 7285-7288.

[7]. Marwein, B. M. R., Sneha, M., & Bharathidasan, I. (2016). A review paper on utilisation of ceramic waste in concrete. International Journal of Scientific & Engineering Research, 7(4), 247-250.

[8]. Onuaguluchi, O., & Banthia, N. (2016). Plant-based natural fibre reinforced cement composites: A review. Cement and Concrete Composites, 68, 96-108.

[9]. Prasad, N. N., Hanitha, P., & Anil, N. C. (2016). Partial replacement of coarse aggregate by crusher tiles and fine aggregate by garanite powder to improve the concrete properties. IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE), 13(6), 168-176.

[10]. Rajalakshmi, P., Suji, D. D., Perarasan, M., & Niranjani, E. (2016). Studies on strength characteristics on utilization of waste ceramic tiles as aggregate in concrete. International Journal of Civil and Structural Engineering Research, 4(1), 114-125.

[11]. Sharma, S., Pandey, P., Bhandari, R., & Shende, A. M. (2022). An empirical research on modified concrete using demolition construction waste as partial substitutes of fine aggregates. Journal of Integrated Science and Technology, 10(3), 185-188.

[12]. Shubbar, S. D., & Al-Shadeedi, A. S. (2017). Utilization of waste plastic bottles as fine aggregate in concrete. Kufa Journal of Engineering, 8(2), 132-146.

[13]. Singh, P., & Singla, R. K. (2015). Utilization of waste ceramic tiles as coarse aggregate in concrete. Journal of Multidisciplinary Engineering Science and Technology (JMEST), 2(11), 3294-3300.

[14]. Topcu, I. B., & Canbaz, M. (2007). Utilization of crushed tile as aggregate in concrete. Iranian Journal of Science & Technology, 31(5), 561-565.

[15]. Tore, V. M., Chawan, P. M., Khedkar, S. A., & Dongarwar, K. (2015). Survey on: Comparison of clustering based feature subset selection algorithms for high dimensional data. International Journal of Innovative Research in Science, Engineering and Technology, 4(12).

[16]. Verendra, C., & Brunda, U. (2020). Investigation on partial replacement of coarse aggregate with ceramic waste and addition of steel fiber in concrete. International Journal for Research in Applied Science & Engineering Technology (IJRASET), 8(5), 1921-1928.

Full Article (HTML)

Pdf

Research Paper

Transportation Planning using Activity-Based Travel Demand Model

Ramesh Kumar*

Department of Civil Engineering, Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India.

Kumar, R. (2023). Transportation Planning using Activity-Based Travel Demand Model. i-manager's Journal on Civil Engineering, 13(2), 30-37. https://doi.org/10.26634/jce.13.2.19944

Abstract

According to Bangalore's Transportation Department, the number of transport vehicles in the city will be close to 85.6 lakh two wheelers, 50 lakh automobiles, and 20 lakh transport vehicles by the end of 2023. Two-wheelers account for 58.41% of all vehicles, while Light Motor Vehicles (LMVs) account for 22%, Heavy Motor Vehicle (HMVs) for 11%, and other vehicles account for 8.59%. Therefore, there is a need for efficient transportation planning. From earlier research, it was found that activity-based modeling is more efficient for evaluation. In this study, a method was proposed to develop an activity-based travel demand model for the selected zones of Bangalore city, and work trips were generated from the Global Tech Park. The trips were monitored, and it was found that people came from all around Bengaluru. Four zones in Bengaluru were chosen for this study because they provided the most travel to the Global Tech Park. The four zones were Kengeri, Mylasandra, RR Nagar, and Vijaynagar. The data were collected through individual person surveys, considering the influential parameters in developing person tours. The collected data were analyzed using SPSS software, and models were developed based on parameters such as age, sex, monthly income, distance traveled, active travel, daily travel cost, and vehicle ownership. The results obtained in the form of the models were compared with those of traditional models. In addition, the factors influencing the trips of each individual were studied, and the effects of these factors were analyzed. The results obtained were satisfactory in terms of the R2 value and other testing parameters. From the primary data analysis, it was found that the preference for modal choices of the public increased from public transport (i.e., buses) to private transport (i.e., owned cars) as per the increase in age, income, professions, travel distance, and daily travel cost.

References

[1]. Altan, M. F., & Ayözen, Y. E. (2018). The effect of the size of traffic analysis zones on the quality of transport demand forecasts and travel assignments. Periodica Polytechnica Civil Engineering, 62(4), 971-979.

[2]. Amavi, A. A., Romero, J. P., Dominguez, A., dell'Olio, L., & Ibeas, A. (2014). Advanced trip generation/attraction models. Procedia-Social and Behavioral Sciences, 160, 430-439.

[3]. Athira, I. C., Muneera, C. P., Krishnamurthy, K., & Anjaneyulu, M. V. L. R. (2016). Estimation of value of travel time for work trips. Transportation Research Procedia, 17, 116-123.

[4]. Clifton, K. J., Currans, K. M., & Muhs, C. D. (2012). Contextual Influences on Trip Generation. Portland State University (pp. 1-84).

[5]. Demissie, M. G., Phithakkitnukoon, S., & Kattan, L. (2018). Trip distribution modeling using mobile phone data: Emphasis on intra-zonal trips. IEEE Transactions on Intelligent Transportation Systems, 20(7), 2605-2617.

[6]. Lee, S., Lee, J., Hiemstra-van Mastrigt, S., & Kim, E. (2022). What cities have is how people travel: Conceptualizing a data-mining-driven modal split framework. Cities, 131, 103902.

[7]. Lekshmi, G. A., Landge, V. S., & Kumar, V. S. (2016). Activity based travel demand modeling of Thiruvananthapuram urban area. Transportation Research Procedia, 17, 498-505.

[8]. Malayath, M., & Verma, A. (2013). Activity based travel demand models as a tool for evaluating sustainable transportation policies. Research in Transportation Economics, 38(1), 45-66.

[9]. Pinjari, A. R., & Bhat, C. R. (2011). Activity-based travel demand analysis. A Handbook of Transport Economics, 10, 213-248.

[10]. Van de Vooren, F. W. C. J. (2004). Modelling transport in interaction with the economy. Transportation Research Part E: Logistics and Transportation Review, 40(5), 417-437.

[11]. Varia, H. R., Prajapati, C. P., & Shah, I. P. (2017). Behavioural analysis of out going trip makers of sabarkantha region, Gujarat, India. International Journal of Engineering Research & Technology, 6(4), 1101-1105.

Full Article (HTML)

Pdf

Review Paper

Fatigue Life Prediction of Concrete Bridges using Wireless Sensors - A Review

V. Guru Rupesh* , B. Vidya**

*-** Department of Civil Engineering, Gayatri Vidya Parishad College for Degree and PG Courses (A), Visakhapatnam, Andhra Pradesh, India.

Rupesh, V. G., and Vidya, B. (2023). Fatigue Life Prediction of Concrete Bridges using Wireless Sensors - A Review. i-manager's Journal on Civil Engineering, 13(2), 38-54. https://doi.org/10.26634/jce.13.2.19841

Abstract

This study evaluates the works carried out for fatigue life prediction of reinforced concrete girders and columns in concrete bridges, utilizing the guidelines outlined by the Indian Road Congress (IRC). To accurately predict fatigue damage on bridges, a dynamic bridge-vehicle interaction model was employed to capture the effects of trafficinduced dynamic loads. The time to the first cracking distribution function was calculated using a dedicated model, enabling the assessment of fatigue life under various amplitude loading scenarios. The model incorporates S-N curves and Miner's rule, which are widely used techniques for measuring fatigue on structures, to evaluate the cumulative damage caused by cyclic loading. Wireless sensors were used in structural monitoring systems to monitor the health of bridges. This paper describes investigations that assess the performance of wireless strain-monitoring devices. These advanced systems offer improved efficiency, cost-effectiveness, and ease of installation compared with traditional monitoring systems. The research presented herein aims to provide a comprehensive approach for evaluating and predicting the fatigue life of reinforced concrete girders and columns in bridges. The proposed model, combined with dynamic bridge-vehicle interaction simulations and wireless strain monitoring devices, offers a robust framework for bridge engineers to effectively assess the structural integrity and maintenance needs of concrete bridges, ultimately enhancing their durability and safety.

References

[1]. Al-Zaid, R. Z., & Nowak, A. S. (2011). Fatigue strength of prestressed concrete girder bridges. Canadian Journal of Civil Engineering, 15(2), 199-205.

[2]. Awad, S. F., Kadhim, F., Aboud, W., & Tahi, M. A. D. (2022). Strain and deformation measurement for prosthetic parts using the Arduino microcontroller and strain gauges instruments. International Journal of Mechanical Engineering, 7(1), 1049-1055.

[3]. Chiewanichakorn, M., Aref, A. J., & Alampalli, S. (2007). Dynamic and fatigue response of a truss bridge with fiber reinforced polymer deck. International Journal of Fatigue, 29(8), 1475-1489.

[4]. Frýba, L., & Gajdoš, L. (1999). Fatigue properties of orthotropic decks on railway bridges. Engineering Structures, 21(7), 639-652.

[5]. Gallucci, L., Menna, C., Angrisani, L., Asprone, D., Schiano Lo Moriello, R., Bonavolontà, F., & Fabbrocino, F. (2017). An embedded wireless sensor network with wireless power transmission capability for the structural health monitoring of reinforced concrete structures. Sensors, 17(11), 2566.

[6]. Imam, B. M., & Righiniotis, T. D. (2010). Fatigue evaluation of riveted railway bridges through global and local analysis. Journal of Constructional Steel Research, 66(11), 1411-1421.

[7]. Li, H., Chen, Y., Liu, J., Zhang, Z., & Zhu, H. (2022). Unmanned aircraft system applications in damage detection and service life prediction for bridges: A review. Remote Sensing, 14(17), 4210.

[8]. Lynch, J. P., & Loh, K. J. (2006). A summary review of wireless sensors and sensor networks for structural health monitoring. Shock and Vibration Digest, 38(2), 91-130.

[9]. Malekjafarian, A., McGetrick, P. J., & OBrien, E. J. (2015). A review of indirect bridge monitoring using passing vehicles. Shock and Vibration, 2015, 1-16.

[10]. Miner, M. A. (1945). Cumulative damage in fatigue. Journal of Applied Mechanics, 12(3), 159-164.

[11]. Mori, T., Lee, H. H., & Kyung, K. S. (2007). Fatigue life estimation parameter for short and medium span steel highway girder bridges. Engineering Structures, 29(10), 2762-2774.

[12]. Mustafa, S., Matsumoto, Y., & Yamaguchi, H. (2018). Vibration-based health monitoring of an existing truss bridge using energy-based damping evaluation. Journal of Bridge Engineering, 23(1), 04017114.

[13]. O'Connor, S., Kim, J., Lynch, J. P., Law, K. H., & Salvino, L. (2010, January). Fatigue life monitoring of metallic structures by decentralized rainflow counting embedded in a wireless sensor network. In Smart Materials, Adaptive Structures and Intelligent Systems, 44168, 751-759.

[14]. Pai, S. G., Nussbaumer, A., & Smith, I. F. (2018). Comparing structural identification methodologies for fatigue life prediction of a highway bridge. Frontiers in Built Environment, 3, 73.

[15]. Rao, V. G., & Talukdar, S. (2003). Prediction of fatigue life of a continuous bridge girder based on vehicle induced stress history. Shock and Vibration, 10(5-6), 325-338.

[16]. Roeder, C. W., MacRae, G., Crocker, P., Arima, K., & Wong, S. (2000). Dynamic response and fatigue of steel tied-arch bridge. Journal of Bridge Engineering, 5(1), 14-21.

[17]. Souza, R. M., Queiroz, R. L., Lobato, S. L. A., Sampaio, R. A. C., & de Azevedo, A. A. (2009). Fatigue life prediction of prestressed and reinforced concrete railway bridges. International Heavy Haul Conference Shanghai.

[18]. Sundaram, A. B., Ravisankar, K., Senthil, R., & Parivallal, S. (2016). Wireless strain sensing system for assessing condition of civil infrastructure facilities. Gradevinar, 68(3), 221-227.

[19]. Tsiatas, G., & Palmquist, S. M. (1999). Fatigue evaluation of highway bridges. Probabilistic Engineering Mechanics, 14(1-2), 189-194.

[20]. Ye, X. W., Su, Y. H., & Han, J. P. (2014). A state-of-theart review on fatigue life assessment of steel bridges. Mathematical Problems in Engineering, 1-13.

i-manager's Journal on Civil Engineering (JCE)

Current Issue Vol. 14 Issue 2

Most Read

Most Cited

Volume 13 Issue 2 April - June 2023

Creep of Concrete Incorporated with Marble Powder

Dr Syed Afzal Basha* , B. Jayarami Reddy**

* G Pullaiah College of Engineering and Technology, Kurnool, Andhra Pradesh, India. ** Rajiv Gandhi University of Knowledge Technologies, Ongole Campus, Andhra Pradesh, India.

Basha, S. A., and Reddy, B. J. (2023). Creep of Concrete Incorporated with Marble Powder. i-manager's Journal on Civil Engineering, 13(2), 1-13. https://doi.org/10.26634/jce.13.2.19834

Abstract

References

Full Article (HTML)

Pdf

Evaluation of Probability Distributions for Estimation of Peak Flood Discharge using FFA Approach

Vivekanandan N.*

Central Water and Power Research Station, Pune, Maharashtra, India.

Vivekanandan, N. (2023). Evaluation of Probability Distributions for Estimation of Peak Flood Discharge using FFA Approach. i-manager's Journal on Civil Engineering, 13(2), 14-20. https://doi.org/10.26634/jce.13.2.19947

Abstract

References

Full Article (HTML)

Pdf

Experimental Investigation on Concrete by Partial Replacement of Fine Aggregate with Ceramic Powder

Ruthrapriya P.* , R. Hariprabam**, P. Ragunathapandian***

*-*** Department of Structural Engineering, Arulmigu Meenakshi Amman College of Engineering, Vadamavandal, Thiruvannamalai District, Tamilnadu, India.

Ruthrapriya, P., Hariprabam, R., and Ragunathapandian, P. (2023). Experimental Investigation on Concrete by Partial Replacement of Fine Aggregate with Ceramic Powder. i-manager's Journal on Civil Engineering, 13(2), 21-29. https://doi.org/10.26634/jce.13.2.19815

Abstract

References

Full Article (HTML)

Pdf

Transportation Planning using Activity-Based Travel Demand Model

Ramesh Kumar*

Department of Civil Engineering, Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India.

Kumar, R. (2023). Transportation Planning using Activity-Based Travel Demand Model. i-manager's Journal on Civil Engineering, 13(2), 30-37. https://doi.org/10.26634/jce.13.2.19944

Abstract

References

Full Article (HTML)

Pdf

Fatigue Life Prediction of Concrete Bridges using Wireless Sensors - A Review

V. Guru Rupesh* , B. Vidya**

*-** Department of Civil Engineering, Gayatri Vidya Parishad College for Degree and PG Courses (A), Visakhapatnam, Andhra Pradesh, India.

Rupesh, V. G., and Vidya, B. (2023). Fatigue Life Prediction of Concrete Bridges using Wireless Sensors - A Review. i-manager's Journal on Civil Engineering, 13(2), 38-54. https://doi.org/10.26634/jce.13.2.19841

Abstract

References

Full Article (HTML)

Pdf

* G Pullaiah College of Engineering and Technology, Kurnool, Andhra Pradesh, India.

** Rajiv Gandhi University of Knowledge Technologies, Ongole Campus, Andhra Pradesh, India.

Ruthrapriya P.* , R. Hariprabam, P. Ragunathapandian*