i-manager Publications

i-manager's Journal on Structural Engineering (JSTE)

Current Issue Vol. 13 Issue 2

Performance Evaluation of High-Rise Diagrid Steel Structures with Different Angle and Base Width
Advancements in CubeSat Development: Applications and Structural Analysis
Analysis of the Erosion and Accretion Dynamics of the Teesta River: Shifting Patterns from 1993 to 2023
Advanced Computational Approaches for Structural Integrity Assessment: Multi-Scale Modelling and Experimental Validation
Transforming Disaster Response: The Role of Agentic AI in Crisis Management

Most Cited

Volume 13 Issue 2 July - September 2024

Research Paper

Performance Evaluation of High-Rise Diagrid Steel Structures with Different Angle and Base Width

Sandeish Dahal* , Satish Paudel**

*Oxford College of Engineering and Management, Pokhara University, Nepal.

**University of Nevada, Reno.

Dahal, S., & Paudel, S. (2024). Performance Evaluation of High-Rise Diagrid Steel Structures with Different Angle and Base Width. i-manager's Journal on Structural Engineering, 13(2), 1-12. https://doi.org/10.26634/jste.13.2.21577

Abstract

This study aims to identify the critical parameters influencing the behavior of the diagrid structure subjected to dynamic wind and seismic loading through numerical approach. The study examines the basic structural behavior of diagrid structural system for different storey modules and with varying base width and finally suggests the optimum diagrid angle for the assumed diagrid models. Along with study of distribution of load in diagrid system a detailed comparison is performed for displacement of top storey, drift, base shear, time period, weight of steel. Also, a non-linear static analysis was performed as per ASCE/SEI 41-13 with proposed modification of the steel diagrid structure for obtaining the capacity of such structures. Finally, after evaluation of the response it can be observed that diagrid structural system consisting of 60° to 75° angle of inclination is the suitable configuration. Also, after the evaluation of such structures numerically it was concluded that the Inner Shear Wall resist the Gravity loads and the Diagrid Module resist Gravity Load as well as Lateral loads.

References

[1]. Applied Technology Council. (1996). Seismic Evaluation and Retrofit of Concrete Buildings.

[2]. Applied Technology Council. (2009). Quantification of Building Seismic Performance Factors.

[3]. Asadi, E., & Adeli, H. (2017). Diagrid: An innovative, sustainable, and efficient structural system. The Structural Design of Tall and Special Buildings, 26(8), e1358.

[4]. Computers and Structures, Inc. (2016). Integrated Solution for Structural Analysis and Design.

[5]. Dat, B. T., Traykov, A., & Traykova, M. (2018). Shear-lag effect and its effect on the design of high-rise buildings. E3S Web of Conferences, 33, 02001.

[6]. Deshpande, R. D., Patil, S. M., & Ratan, S. (2015). Analysis and comparison of diagrid and conventional structural system. International Research Journal of Engineering and Technology, 2(3), 2295-2300.

[7]. FEMA-356. (2000). Prestandard and Commentary for the Seismic Rehabilitation of Buildings.

[8]. Bureau of Indian Standard. (2002). Criteria for Earthquake Resistant Design of Structures; General Provisions and Buildings IS: 1893 (Part 1)-2002; New Delhi, India.

[9]. Bureau of Indian Standard. (1987). Code of Practice for Design Loads (Other than Earthquake) for Buildings and Structures IS: 875 (Part 2) – 1987, (Reaffirmed 2003), New Delhi, India.

[10]. Bureau of Indian Standard. (2000). Code of Practice for Plain and Reinforced Concrete IS: 456: 2000, (Reaffirmed 2005), New Delhi, India.

[11]. Bureau of Indian Standard. (2007). General Construction in Steel-Code of Practice (IS: 800-2007), New Delhi, India.

[12]. Jani, K. D., & Patel, P. V. (2013). Design of diagrid structural system for high rise steel buildings as per Indian standards. In Structures Congress 2013: Bridging Your Passion with Your Profession (pp. 1070-1081).

[13]. Kim, J., & Lee, Y. H. (2012). Seismic performance evaluation of diagrid system buildings. The Structural Design of Tall And Special Buildings, 21(10), 736-749.

[14]. Kim, Y. J., Jung, I. Y., Ju, Y. K., Park, S. J., & Kim, S. D. (2010). Cyclic behavior of diagrid nodes with H-section braces. Journal of Structural Engineering, 136(9), 1111-1122.

[15]. Lee, J., Kong, J., & Kim, J. (2018). Seismic performance evaluation of steel diagrid buildings. International Journal of Steel Structures, 18, 1035-1047.

[16]. Leonard, J. (2007). Investigation of shear lag effect in high-rise buildings with diagrid system (Doctoral dissertation, Massachusetts Institute of Technology, Cambridge, USA).

[17]. Liu, C., Li, Q., Lu, Z., & Wu, H. (2017). A review of the diagrid structural system for tall buildings. The Structural Design of Tall and Special Buildings, 27(4), e1445.

[18]. Mele, E., Toreno, M., Brandonisio, G., & De Luca, A. (2014). Diagrid structures for tall buildings: Case studies and design considerations. The Structural Design of Tall and Special Buildings, 23(2), 124-145.

[19]. Mendis, P., Ngo, T., Haritos, N., Hira, A., Samali, B., & Cheung, J. (2007). Wind loading on tall buildings. Electronic Journal of Structural Engineering, 7, 41–54.

[20]. Milana, G., Gkoumas, K., & Bontempi, F. (2014, August). Sustainability concepts in the design of high-rise buildings: The case of diagrid systems. In Proceedings of the 3rd International Workshop on Design in Civil and Environmental Engineering (pp. 21-23).

[21]. Moon, K. S., Connor, J. J., & Fernandez, J. E. (2007). Diagrid structural systems for tall buildings: characteristics and methodology for preliminary design. The Structural Design of Tall and Special Buildings, 16(2), 205-230.

[22]. Moon, K.S. (2011). Diagrid structures for complex-shaped tall buildings. The Twelfth East Asia-Pacific Conference on Structural Engineering and Construction, 14, 1343–1350.

[23]. Panchal, N. B., & Patel, V. R. (2014). Diagrid structural system: Strategies to reduce lateral forces on high-rise buildings. International Journal of Research in Engineering and Technology, 3(03), 374-378.

[24]. Patil, G. R., & Potdar, A. K. (2017). Optimum design of concrete diagrid building and its comparison with conventional frame building. International Research Journal of Engineering and Technology, 4(8), 1471-1476.

[25]. Shadhan, K. K. (2015). Optimal diagrid angle to minimize drift in high-rise steel buildings subjected to wind loads. International Journal of Civil Engineering and Technology, 6(11), 1-10.

[26]. Shankar, B., Dheekshith, K., & Hijaz S. N. (2017). Study on behavior of diagrids under seismic loads compared to conventional moment resisting frames. International Research Journal of Engineering and Technology, 4(8), 1636–1640.

[27]. Shi, Q., & Zhang, F. (2019). Simplified calculation of shear lag effect for high-rise diagrid tube structures. Journal of Building Engineering, 22, 486-495.

[28]. Singh, Y., & Nagpal, A. K. (1994). Negative shear lag in framed-tube buildings. Journal of Structural Engineering, 120(11), 3105-3121.

[29]. Tank, J. B., & Hansora, A. G. (2016). Varying angle diagrid structural system. International Journal for Scientific Research and Development, 4(3), 527-529.

[30]. Thomas, F. M., Issac, B. M., & George, J. (2015, September). Performance evaluation of tall buildings with steel diagrid system. In 2nd International Conference on Science, Technology and Management (pp. 2242-2256).

[31]. Tripathi, H., & Singla, D. S. (2016). Diagrid structural system for RC framed multistoreyed buildings. International Journal of Scientific and Engineering Research, 7(6), 356-362.

[32]. Vinay, A. C., & Hedge, M. N. (2017). Comparative analysis of conventional steel structure with diagrid structures of varied angles. International Research Journal of Engineering and Technology, 4(9), 1154–1159.

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Research Paper

Advancements in CubeSat Development: Applications and Structural Analysis

Sri Ram Deepak Akella* , Sashendra Srinivas Baswanth Pappula**

*Department of Aerospace Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, India.

**Cadmax Solutions Private Limited, Bangalore, India.

Akella, S. R. D., & Pappula, S. S. B. (2024). Advancements in CubeSat Development: Applications and Structural Analysis. i-manager's Journal on Structural Engineering, 13(2), 13-24. https://doi.org/10.26634/jste.13.2.21618

Abstract

This paper explores the development, applications, and structural analysis of CubeSats, small standardized satellites that have revolutionized space missions through their affordability and versatility. Initiated by professors Jardi Puig-Suari and Bob Twiggs in 1999, CubeSats were designed for educational and research purposes, enabling the testing of space technologies in low Earth orbit (LEO). Initially dominated by academic institutions, CubeSat launches saw significant growth from commercial and amateur sectors by 2014. CubeSats have expanded from LEO operations to interplanetary missions, demonstrating their adaptability for remote sensing, communications, and scientific research. This literature review highlights CubeSat advancements, emphasizing their standardized 1U, 2U, and 3U configurations, each with distinct capabilities and challenges. Structural analysis using materials like aluminum alloy, titanium alloy, FR-4, and stainless-steel focuses on their performance under static and dynamic loads. Modal and structural analyses reveal that materials such as aluminum alloy and stainless-steel offer high performance, with significant implications for optimizing CubeSat designs. The findings underscore CubeSats’ potential for enhancing Space science education, supporting technology development, and fostering space exploration in both developed and emerging nations.

References

[1]. Ampatzoglou, A., & Kostopoulos, V. (2018). Design, analysis, optimization, manufacturing, and testing of a 2U CubeSat. International Journal of Aerospace Engineering, 2018(1), 9724263.

[2]. Aswin, M. R., Pavithran, A., Mangrole, Y., & Ravi, B. (2022, November). Structural and thermal analysis of a CubeSat. In Conference of Innovative Product Design and Intelligent Manufacturing System (pp. 363-371). Singapore: Springer Nature Singapore.

[3]. Cihan, M., Haktanır, O. O., Akbulut, I., & Aslan, A. R. (2008, June). Flight dynamic analysis of ITUpSAT1. In International Workshop on Small Satellites, New Missions and New Technologies.

[4]. Condruz, M. R., Voicu, L. R., Puscasu, C., Vintila, I. S., Sima, M., Deaconu, M., & Dragasanu, L. (2017). Composite material designs for lightweight space packaging structures. In Aerospace Europe CEAS 2017 Conference, Bucharest, Romania (pp. 1-12).

[5]. Hernandez, V., Ravindran, A., & Herras-Martinez, M. (2017). On-orbit demonstration of the space weather and meteor impact monitoring network. In 31st Annual AIAA/USC Conference on Small Satellites.

[6]. Inamdar, K., Milani, H., Garcia, A., Antonello, A., & De Quattro, N. (2016). Cubesats as platform for remote sensing applications with satellite navigation signal. In 67th International Astronautical Congress 2016, Guadalajara, Mexico (pp. 1-9).

[7]. Kelley, R. L., & Jarkey, D. R. (2015). Cubesat material. Springer International Publishing.

[8]. Lima, V. H. L. C., Rodrigues, R. D. N., Lamary, P. M. C., & Bezerra, R. D. A. (2022). Dynamic structural analysis in a 2U CubeSat considering quasi-static loads. Revista Chilena De Ingeniería, 30(1), 94-108.

[9]. Phalphale, A., Sanghvi, N., Dingare, S. V., & Sen, S. (2022, December). Investigation of composite material for a remote sensing CubeSat structure: An alternative to traditional aluminum alloys. In International Conference Congress of the Indian Society of Theoretical and Applied Mechanics (pp. 273-284). Singapore: Springer Nature Singapore.

[10]. Puig-Suari, J., Turner, C., & Ahlgren, W. (2001, March). Development of the standard CubeSat deployer and a CubeSat class PicoSatellite. In 2001 IEEE Aerospace Conference Proceedings (Cat. No. 01TH8542), 1, 1-347. IEEE.

[11]. Qiao, L., Rizos, C., & Dempster, A. G. (2013, July). Analysis and comparison of CubeSat lifetime. In Proceedings of the 12th Australian Space Conference (pp. 249-260). Citeseer.

[12]. Qureshi, A. A., Afzal, M. U., Tauqeer, T., & Tarar, M. A. (2011, April). Performance analysis of FR-4 substrate for high frequency microstrip antennas. In 2011 China-Japan Joint Microwave Conference (pp. 1-4). IEEE.

[13]. Rohan, S., Saju, S. K., Syam, S., & Shabna. (2023). Design and development of a CubeSat for atmospheric research. REST Journal on Emerging Trends in Modelling and Manufacturing, 9(2), 1-8.

[14]. Santilli, G., Vendittozzi, C., Cappelletti, C., Battistini, S., & Gessini, P. (2018). CubeSat constellations for disaster management in remote areas. Acta Astronautica, 145, 11-17.

[15]. Shiroma, W. A., Martin, L. K., Akagi, J. M., Akagi, J. T., Wolfe, B. L., Fewell, B. A., & Ohta, A. T. (2011). CubeSats: A bright future for nanosatellites. Central European Journal of Engineering, 1(1), 9-15.

[16]. Swartwout, M. (2013). The first one hundred cubesats: A statistical look. Journal of Small Satellites, 2(2), 213-233.

[17]. Woellert, K., Ehrenfreund, P., Ricco, A. J., & Hertzfeld, H. (2011). Cubesats: Cost-effective science and technology platforms for emerging and developing nations. Advances in Space Research, 47(4), 663-684.

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Research Paper

Analysis of the Erosion and Accretion Dynamics of the Teesta River: Shifting Patterns from 1993 to 2023

Md. Hazrat Ali* , Md. Redwanur Rahman, Md. Imran Hossain, Md. Razi Uddin, Mst. Rinu Fatema

*,**Directorate of Secondary and Higher Secondary, Ministry of Education, Bangladesh.

,*Institute of Environmental Science, Rajshahi University, Bangladesh.

***Department of Tourism and Hospitality Management, Noakhali Science and Technology University, Bangladesh.

Ali, M. H., Rahman, M. R., Hossain, M. I., Uddin, M. R., & Fatema, M. R. (2024). Analysis of the Erosion and Accretion Dynamics of the Teesta River: Shifting Patterns from 1993 to 2023. i-manager's Journal on Structural Engineering, 13(2), 25-34. https://doi.org/10.26634/jste.13.2.21653

Abstract

The Teesta is one of the most dynamic river systems in Bangladesh. This study aims to investigate the erosion and accretion processes along the Teesta River in Bangladesh within a designated reach from 1993 to 2023. The bank line migration was delineated using Landsat TM and OLI satellite images taken in 1993, 2003, 2013, and 2023 with a resolution of 30 meters and WRS_PATH, WRS_ROW 138,042. Water level analysis was conducted using the hydrological water level data (1993-2023) obtained from the Bangladesh Water Development Board (BWDB). The spatial movement of the river bank line was examined using remote sensing (RS) and geographical information system (GIS) techniques. The trend analysis of the hydrological parameters was conducted using exploratory data analysis techniques. In contrast, various ArcGIS and Microsoft Excel tools were used to estimate the accretion and erosion area of Laimonirhat from satellite images. The findings indicate that erosion and accretion processes are significantly impacted by variations in water levels. These procedures show how channel shifting is dynamic and responds to changes in the bank line. Depending on the degree of accretion and erosion at various cross-sections, the river may narrow or occasionally enlarge. The results show that accretion and erosion took place every year, changing the river area and the migration of the river channels.

References

[1]. Ahmed, I. (2015). People of Many Rivers: Tales from the Riverbanks. University Press Limited.

[2]. Aktar, N. (2012). Prediction of River Bank Erosion along the Jamuna, the Ganges the Padma and the Lower Meghna Rivers in 2012. Center for Environmental and Geographic Information Services.

[3]. Alam, G. M. (2016). An Assessment of the Livelihood system in Bangladesh using Landsat and hydrological data. Geomorphology, 276, 257-279.

[4]. Allison, M. A. (1998). Geologic framework and environmental status of the Ganges-Brahmaputra Delta. Journal of Coastal Research (pp. 827-836).

[5]. Billah, M. M. (2018). Mapping and monitoring erosion-accretion in an alluvial river using satellite imagery–the river bank changes of the Padma river in Bangladesh. Quaestiones Geographicae, 37(3), 87-95.

[6]. Buffington, J. M. (2012). Changes in channel morphology over human time scales. Gravel bed Rivers: Processes, Tools, Environments (pp. 433-463).

[7]. Dewan, A., Corner, R., Saleem, A., Rahman, M. M., Haider, M. R., Rahman, M. M., & Sarker, M. H. (2017). Assessing channel changes of the Ganges-Padma River.

[8]. Eaton, B. C., Millar, R. G., & Davidson, S. (2010). Channel patterns: Braided, anabranching, and single-thread. Geomorphology, 120(3-4), 353-364.

[9]. Ferdous, J., & Mallick, D. (2019). Norms, practices, and gendered vulnerabilities in the lower Teesta basin, Bangladesh. Environmental Development, 31, 88-96.

[10]. Gupta, N., Atkinson, P. M., & Carling, P. A. (2013). Decadal length changes in the fluvial planform of the River Ganga: Bringing a mega-river to life with Landsat archives. Remote Sensing Letters, 4(1), 1-9.

[11]. Heitmuller, F. T. (2014). Channel adjustments to historical disturbances along the lower Brazos and Sabine Rivers, south-central USA. Geomorphology, 204, 382-398.

[12]. Hossain, M. M., Baki, A. B. M., & Ahmad, S. S. (2008, May). The flood problem and mitigation methods in Bangladesh. In 4th International Symposium on Flood Defense.

[13]. Hutton, D., & Haque, C. E. (2003). Patterns of coping and adaptation among erosion-induced displacees in Bangladesh: Implications for hazard analysis and mitigation. Natural Hazards, 29, 405-421.

[14]. Islam, A. R. M. T. (2016). Assessment of fluvial channel dynamics of Padma River in Northwestern Bangladesh. Universal Journal of Geoscience, 4(2), 41-49.

[15]. Karmakar, S., & Rahman, M. M. (2019). Study on the changing patterns of climate near Teesta River in Bangladesh and its impacts on livelihoods. The Journal of NOAMI, 36(1-2), 45-61.

[16]. Khan, M. S. (2015). Anthropogenic impact on morphology of Teesta River in Northern Bangladesh: an exploratory study. J Geosci Geomat, 3(3), 50.

[17]. Kummu, M., Lu, X. X., Rasphone, A., Sarkkula, J., & Koponen, J. (2008). Riverbank changes along the Mekong River: Remote sensing detection in the Vientiane–Nong Khai area. Quaternary International, 186(1), 100-112.

[18]. Lein, H. (2000). Hazards and 'forced' migration in Bangladesh. Norsk Geografisk Tidsskrift-Norwegian Journal of Geography, 54(3), 122-127.

[19]. Makenro, M. B. (2000). World Disaster Report, 2001. International Federation of the Red Cross and Red Crescent Societies (IFRCS), Geneva.

[20]. Midha, N., & Mathur, P. K. (2014). Channel characteristics and planform dynamics in the Indian Terai, Sharda River. Environmental Management, 53, 120-134.

[21]. Mutton, D., & Haque, C. E. (2004). Human vulnerability, dislocation and resettlement: Adaptation processes of river bank erosion induced displacees in Bangladesh. Disasters, 28(1), 41-62.

[22]. Nanson, G. C., & Knighton, A. D. (1996). Anabranching rivers: Their cause, character and classification. Earth Surface Processes and Landforms, 21(3), 217-239.

[23]. Petts, G. E., & Gurnell, A. M. (2005). Dams and geomorphology: Research progress and future directions. Geomorphology, 71(1-2), 27-47.

[24]. Rahman, M. M., Arya, D. S., Goel, N. K., & Dhamy, A. P. (2011). Design flow and stage computations in the Teesta River, Bangladesh, using frequency analysis and MIKE 11 modeling. Journal of Hydrologic Engineering, 16(2), 176-186.

[25]. Ramos, J., & Gracia, J. (2012). Spatial–temporal fluvial morphology analysis in the Quelite river: It's impact on communication systems. Journal of Hydrology, 412, 269-278.

[26]. Rozo, M. G., Nogueira, A. C., & Castro, C. S. (2014). Remote sensing-based analysis of the planform changes in the Upper Amazon River over the period 1986–2006. Journal of South American Earth Sciences, 51, 28-44.

[27]. Sarker, M. H., Thorne, C. R., Aktar, M. N., & Ferdous, M. R. (2014). Morpho-dynamics of the Brahmaputra- Jamuna river, Bangladesh. Geomorphology, 215, 45-59.

[28]. Sarma, J. N. (2005). Fluvial process and morphology of the Brahmaputra River in Assam, India. Geomorphology, 70(3-4), 226-256.

[29]. Sinha, R., & Ghosh, S. (2012). Understanding dynamics of large rivers aided by satellite remote sensing: a case study from Lower Ganga plains, India. Geocarto International, 27(3), 207-219.

[30]. Sultana, R., & Paul, S. K. (2022). Exploring the impacts of river morphology change associated natural disasters on Teesta Riparian Environment of Bangladesh. In Climate, Environment and Disaster in Developing Countries (pp. 361-373). Springer Nature Singapore.

[31]. Wang, S., Li, L., Ran, L., & Yan, Y. (2016). Spatial and temporal variations of channel lateral migration rates in the Inner Mongolian reach of the upper Yellow River. Environmental Earth Sciences, 75, 1-14.

[32]. Winterbottom, S. J. (2000). Medium and short-term channel planform changes on the Rivers Tay and Tummel, Scotland. Geomorphology, 34(3-4), 195-208.

[33]. Yao, Z., Ta, W., Jia, X., & Xiao, J. (2011). Bank erosion and accretion along the Ningxia–Inner Mongolia reaches of the Yellow River from 1958 to 2008. Geomorphology, 127(1-2), 99-106.

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Research Paper

Advanced Computational Approaches for Structural Integrity Assessment: Multi-Scale Modelling and Experimental Validation

Charmie Rodriguez*

Department of Civil Engineering, Cebu Technological University, Cebu, Philippines.

Rodriguez, C. (2024). Advanced Computational Approaches for Structural Integrity Assessment: Multi-Scale Modelling and Experimental Validation. i-manager's Journal on Structural Engineering, 13(2), 35-47. https://doi.org/10.26634/jste.13.2.21670

Abstract

Structural integrity assessment plays a crucial role in engineering, aerospace, and automotive industries, ensuring the reliability and durability of materials and components under various loading conditions. This research presents a multi-scale computational approach integrating finite element analysis (FEA), damage mechanics, and experimental validation techniques to enhance the predictive modelling of structural failure mechanisms. The study focuses on fracture mechanics, fatigue behaviour, and the influence of material microstructures on macroscopic failure patterns. Advanced machine learning algorithms are incorporated to optimize computational efficiency and improve failure prediction accuracy. The results demonstrate that integrating microstructural modelling with experimental data significantly enhances predictive capabilities, leading to safer and more reliable structural designs. The paper also highlights emerging challenges and future directions in computational structural integrity assessments.

References

[1]. Alshaikh, I. M., Bakar, B. A., Alwesabi, E. A., & Akil, H. M. (2020, June). Experimental investigation of the progressive collapse of reinforced concrete structures: An overview. In Structures, 25, 881-900. Elsevier.

[2]. Benjeddou, A. (2000). Advances in piezoelectric finite element modeling of adaptive structural elements: A survey. Computers & Structures, 76(1-3), 347-363.

[3]. Castaldo, P., Gino, D., Marano, G. C., & Mancini, G. (2022). Aleatory uncertainties with global resistance safety factors for non-linear analyses of slender reinforced concrete columns. Engineering Structures, 255, 113920.

[4]. Chen, Y., Zhang, L., & Li, X. (2023). Machine learning applications in structural failure prediction. Engineering Failure Analysis, 52(3), 421-439.

[5]. Figueira, G., & Almada-Lobo, B. (2014). Hybrid simulation–optimization methods: A taxonomy and discussion. Simulation Modelling Practice and Theory, 46, 118-134.

[6]. Frangopol, D. M. (1995). Reliability-based optimum structural design. In Probabilistic Structural Mechanics Handbook: Theory and Industrial Applications (pp. 352-387).

[7]. Ghosn, M., Frangopol, D. M., McAllister, T. P., Shah, M., Diniz, S. M. C., Ellingwood, B. R., & Zhao, X. L. (2016). Reliability-based performance indicators for structural members. Journal of Structural Engineering, 142(9), F4016002.

[8]. Gu, X. L., Zhang, B., Wang, Y., & Wang, X. L. (2021). Experimental investigation and numerical simulation on progressive collapse resistance of RC frame structures considering beam flange effects. Journal of Building Engineering, 42, 102797.

[9]. Hartono, W., Kristiawan, S. A., Handayani, D., & Sutopo, W. (2025). Risk Assessment with Monte Carlo Simulation to Improve Bridge Construction Safety. Engineering Proceedings, 84(1), 56.

[10]. Jayasinghe, S. C., Mahmoodian, M., Sidiq, A., Nanayakkara, T. M., Alavi, A., Mazaheri, S., & Setunge, S. (2024). Innovative digital twin with artificial neural networks for real-time monitoring of structural response: A port structure case study. Ocean Engineering, 312, 119187.

[11]. Li, B., Zhang, J., Qu, Y., Chen, D., & Chen, F. (2024). Data-driven predicting of bond strength in corroded BFRP concrete structures. Case Studies in Construction Materials, 21, e03638.

[12]. Liu, X., Athanasiou, C. E., Padture, N. P., Sheldon, B. W., & Gao, H. (2020). A machine learning approach to fracture mechanics problems. Acta Materialia, 190, 105-112.

[13]. Melchers, R. E., & Beck, A. T. (2018). Structural Reliability Analysis and Prediction. John Wiley & Sons.

[14]. Mengesha, G. (2024). Integrating AI in Structural Health Monitoring (SHM): A Systematic Review on Advances, Challenges, and Future Directions. Structural Health Monitoring (SHM): A Systematic Review on Advances, Challenges, and Future.

[15]. Nguyen, T. T., Yvonnet, J., Zhu, Q. Z., Bornert, M., & Chateau, C. (2016). A phase-field method for computational modeling of interfacial damage interacting with crack propagation in realistic microstructures obtained by microtomography. Computer Methods in Applied Mechanics and Engineering, 312, 567-595.

[16]. Ni, P., Li, J., Hao, H., Yan, W., Du, X., & Zhou, H. (2020). Reliability analysis and design optimization of nonlinear structures. Reliability Engineering & System Safety, 198, 106860.

[17]. Qin, W., Chen, E. J., Wang, F., Liu, W., & Zhou, C. (2024). Data-driven models in reliability analysis for tunnel structure: A systematic review. Tunnelling and Underground Space Technology, 152, 105928.

[18]. Raghu Prasad, B. K., Bharatkumar, B. H., Ramachandra Murthy, D. S., Narayanan, R., & Gopalakrishnan, S. (2005). Fracture mechanics model for analysis of plain and reinforced high-performance concrete beams. Journal of Engineering Mechanics, 131(8), 831-838.

[19]. Rusek, J., Firek, K., & Słowik, L. (2020). Extracting structure of bayesian network from data in predicting the damage of prefabricated reinforced concrete buildings in mining areas. Eksploatacja i Niezawodność, 22(4), 658-666.

[20]. Saatci, S., & Vecchio, F. J. (2009). Nonlinear finite element modeling of reinforced concrete structures under impact loads. ACI Structural Journal, 106(5), 717.

[21]. Sabato, A., Dabetwar, S., Kulkarni, N. N., & Fortino, G. (2023). Noncontact sensing techniques for AI-aided structural health monitoring: A systematic review. IEEE Sensors Journal, 23(5), 4672-4684.

[22]. Sedmak, A. (2018). Computational fracture mechanics: An overview from early efforts to recent achievements. Fatigue & Fracture of Engineering Materials & Structures, 41(12), 2438-2474.

[23]. Swait, T. J., Rauf, A., Grainger, R., Bailey, P. B., Lafferty, A. D., Fleet, E. J., & Hayes, S. A. (2012). Smart composite materials for self-sensing and self-healing. Plastics, Rubber and Composites, 41(4-5), 215-224.

[24]. Vecchio, F. J. (2001). Non-linear finite element analysis of reinforced concrete: At the crossroads?. Structural Concrete, 2(4), 201-212.

[25]. Zhang, H., Mullen, R. L., & Muhanna, R. L. (2010). Interval Monte Carlo methods for structural reliability. Structural Safety, 32(3), 183-190.

[26]. Zhao, J., & DeJong, M. (2023). Three-dimensional probabilistic assessment of tunneling induced structural damage using Monte-Carlo method and hybrid finite element model. Computers and Geotechnics, 154, 105122.

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Research Paper

Transforming Disaster Response: The Role of Agentic AI in Crisis Management

Ushaa Eswaran* , J. Suman, Jaishee*, Manisha**, Jeevitha*, Kowshika, Kesavan***

,,Department of Electronics and Communication Engineering, Mahalakshmi Tech Campus, Chennai, Tamilnadu, India.

Mahalakshmi Vidya Mandit, Chennai, Tamilnadu, India.

_Department of Artificial Intelligence and Data Science, Mahalakshmi Tech Campus, Chennai, Tamilnadu, India.

****Department of Computer Science and Engineering, Mahalakshmi Tech Campus, Chennai, Tamilnadu, India.

Eswaran, U., Suman, J., Jaishee, Manisha, Jeevitha, Kowshika, & Kesavan. (2024). Transforming Disaster Response: The Role of Agentic AI in Crisis Management. i-manager's Journal on Structural Engineering, 13(2), 48-62. https://doi.org/10.26634/jste.13.2.21675

Abstract

One revolutionary step in redefining disaster response procedures is the use of agentic AI in crisis management. Conventional methods of disaster management mostly depend on human judgement, which is frequently sluggish, prone to mistakes, and overpowered by the complexity of ever-changing emergency situations. A new paradigm for handling such difficulties is provided by agentic AI, which is distinguished by its capacity for autonomous decision-making, adaptive learning, and real-time data processing. This paper examines how agentic AI can be incorporated into disaster response systems, emphasising how it can automate crucial decision-making, maximise resource allocation, and offer real-time insights in emergency scenarios. We explore the underlying technologies, including natural language processing (NLP), machine learning, and multi-agent systems, and show how they can be used to improve situational awareness, coordination, and the precision of decisions. We offer experimental data demonstrating the effectiveness of Agentic AI in enhancing resource distribution efficiency and disaster response times using mathematical modelling. Furthermore, we provide case studies from both man-made and natural disasters to highlight the practical benefits and difficulties of implementing such systems. We describe the possible development of AI-driven crisis management systems by talking about prospective trends, touching on scalability and ethical issues. With insights into its real-world uses and future potential to provide more robust, efficient, and effective disaster response frameworks, this chapter provides a thorough knowledge of how agentic AI might reinvent crisis management.

References

[1]. Abhinaya, P., Reddy, C. K. K., Ranjan, A., & Ozer, O. (2024). Explicit monitoring and prediction of hailstorms with xgboost classifier for sustainability. In AI and IoT for Proactive Disaster Management (pp. 107-132).

[2]. Aboualola, M., Abualsaud, K., Khattab, T., Zorba, N., & Hassanein, H. S. (2023). Edge technologies for disaster management: A survey of social media and artificial intelligence integration. IEEE Access, 11, 73782-73802.

[3]. Al Shafian, S., & Hu, D. (2024). Integrating machine learning and remote sensing in disaster management: A decadal review of post-disaster building damage assessment. Buildings, 14(8), 2344.

[4]. Albahri, A. S., Khaleel, Y. L., Habeeb, M. A., Ismael, R. D., Hameed, Q. A., Deveci, M., & Alzubaidi, L. (2024). A systematic review of trustworthy artificial intelligence applications in natural disasters. Computers and Electrical Engineering, 118, 109409.

[5]. Aref, M. M. (2003, September). A multi-agent system for natural language understanding. In IEMC'03 Proceedings. Managing Technologically Driven Organizations: The Human Side of Innovation and Change (pp. 36-40).

[6]. Biswas, S., Kumar, D., Hajiaghaei-Keshteli, M., & Bera, U. K. (2024). An AI-based framework for earthquake relief demand forecasting: A case study in Türkiye. International Journal of Disaster Risk Reduction, 102, 104287.

[7]. Chan, A., Ezell, C., Kaufmann, M., Wei, K., Hammond, L., Bradley, H., & Anderljung, M. (2024, June). Visibility into AI agents. In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (pp. 958-973).

[8]. Dumitrescu, C., Radu, V., Gheorghe, R., Tabîrca, A. I., Stefan, M. C., & Manea, L. (2024). Crowd panic behavior simulation using multi-agent modeling. Electronics, 13(18), 3622.

[9]. Eswaran, U., Eswaran, V., Murali, K., & Eswaran, V. (2024a). Quantum-Based predictive modeling for extreme weather events. In The Rise of Quantum Computing in Industry 6.0 Towards Sustainability (pp. 123-140).

[10]. Eswaran, U., Eswaran, V., Murali, K., & Eswaran, V. (2024b). Human-centric AI balancing innovation with ethical considerations in the age of soft computing. In Soft Computing in Industry 5.0 for Sustainability (pp. 87-116).

[11]. Eswaran, U., Eswaran, V., Murali, K., & Eswaran, V. (2024c). Security, risk management, and ethical AI in the future of DeFi. In AI-Driven Decentralized Finance and the Future of Finance (pp. 48-88).

[12]. Gupta, B. B., & Wu, J. (2025). Integration of IoT with robotics and drones. In AI Developments for Industrial Robotics and Intelligent Drones (pp. 33-54).

[13]. Hu, R., Zhou, K., Yang, J., & Yin, H. (2024). Management of resilient urban integrated energy system: State-of-the-art and future directions. Journal of Environmental Management, 363, 121318.

[14]. Kirpalani, C. (2024). Technology Driven approaches to enhance disaster response and recovery. Geospatial Technology for Natural Resource Management, 25-81.

[15]. Kopanov, K., Varbanov, V., & Atanasova, T. (2025). Leveraging social media data and artificial intelligence for improving earthquake response efforts. In The International Conference on Environmental Protection and Disaster Risks, 883, 218-225.

[16]. MahmoudZadeh, S., Yazdani, A., Kalantari, Y., Ciftler, B., Aidarus, F., & Al Kadri, M. O. (2024). Holistic review of UAV-centric situational awareness: Applications, limitations, and algorithmic challenges. Robotics, 13(8), 117.

[17]. Miller, M., Chowdhury, S., Alzarrad, A., & Hossain, N. U. I. (2024). Synergetic decision-making: Analyzing the interplay of human behavior and physical infrastructure in emergency evacuations via an analytical approach. Global Journal of Flexible Systems Management, (pp.785–803).

[18]. Nagaiah, K., Kalaivani, K., Palamalai, R., Suresh, K., Sethuraman, V., & Karuppiah, V. (2024). A logical remote sensing based disaster management and alert system using AI-assisted internet of things technology. Remote Sensing in Earth Systems Sciences, 7(4), 457-471.

[19]. North, M. P., Bisbing, S. M., Hankins, D. L., Hessburg, P. F., Hurteau, M. D., Kobziar, L. N., & Stevens-Rumann, C. S. (2024). Strategic fire zones are essential to wildfire risk reduction in the Western United States. Fire Ecology, 20(1), 50.

[20]. Rajkumar, R., Lee, C., Lehoczky, J., & Siewiorek, D. (1997, December). A resource allocation model for QoS management. In Proceedings Real-Time Systems Symposium (pp. 298-307).

[21]. Siddiqui, M. A. (2024). A comprehensive review of AI: Ethical Frameworks, Challenges, and Development. Adhyayan: A Journal of Management Sciences, 14(01), 68-75.

[22]. Singh, V., & Sharma, S. K. (2025). Role of multi-agent system in reducing failure probabilities in production system. In Enhancing Automated Decision-Making Through AI (pp. 211-246).

[23]. Tagarev, T., & Ratchev, V. (2020). A taxonomy of crisis management functions. Sustainability, 12(12), 5147.

[24]. Torkjazi, M., & Raz, A. K. (2024). A review on integrating autonomy into system of systems: Challenges and research directions. IEEE Open Journal of Systems Engineering. 2, 157-178.

[25]. Yön, B., Dedeoğlu, İ. Ö., Yetkin, M., Erkek, H., & Calayır, Y. (2024). Evaluation of the seismic response of reinforced concrete buildings in the light of lessons learned from the February 6, 2023, Kahramanmaraş, Türkiye earthquake sequences. Natural Hazards, 121, 873–909.

i-manager's Journal on Structural Engineering (JSTE)

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Volume 13 Issue 2 July - September 2024

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*Oxford College of Engineering and Management, Pokhara University, Nepal. **University of Nevada, Reno.

Dahal, S., & Paudel, S. (2024). Performance Evaluation of High-Rise Diagrid Steel Structures with Different Angle and Base Width. i-manager's Journal on Structural Engineering, 13(2), 1-12. https://doi.org/10.26634/jste.13.2.21577

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*Department of Aerospace Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, India. **Cadmax Solutions Private Limited, Bangalore, India.

Akella, S. R. D., & Pappula, S. S. B. (2024). Advancements in CubeSat Development: Applications and Structural Analysis. i-manager's Journal on Structural Engineering, 13(2), 13-24. https://doi.org/10.26634/jste.13.2.21618

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Analysis of the Erosion and Accretion Dynamics of the Teesta River: Shifting Patterns from 1993 to 2023

Md. Hazrat Ali* , Md. Redwanur Rahman**, Md. Imran Hossain***, Md. Razi Uddin****, Mst. Rinu Fatema*****

*,****Directorate of Secondary and Higher Secondary, Ministry of Education, Bangladesh. **,***Institute of Environmental Science, Rajshahi University, Bangladesh. *****Department of Tourism and Hospitality Management, Noakhali Science and Technology University, Bangladesh.

Ali, M. H., Rahman, M. R., Hossain, M. I., Uddin, M. R., & Fatema, M. R. (2024). Analysis of the Erosion and Accretion Dynamics of the Teesta River: Shifting Patterns from 1993 to 2023. i-manager's Journal on Structural Engineering, 13(2), 25-34. https://doi.org/10.26634/jste.13.2.21653

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Advanced Computational Approaches for Structural Integrity Assessment: Multi-Scale Modelling and Experimental Validation

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Transforming Disaster Response: The Role of Agentic AI in Crisis Management

Ushaa Eswaran* , J. Suman**, Jaishee***, Manisha****, Jeevitha*****, Kowshika******, Kesavan*******

Eswaran, U., Suman, J., Jaishee, Manisha, Jeevitha, Kowshika, & Kesavan. (2024). Transforming Disaster Response: The Role of Agentic AI in Crisis Management. i-manager's Journal on Structural Engineering, 13(2), 48-62. https://doi.org/10.26634/jste.13.2.21675

Abstract

References

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*Oxford College of Engineering and Management, Pokhara University, Nepal.

**University of Nevada, Reno.

*Department of Aerospace Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, India.

**Cadmax Solutions Private Limited, Bangalore, India.

Md. Hazrat Ali* , Md. Redwanur Rahman, Md. Imran Hossain, Md. Razi Uddin, Mst. Rinu Fatema

*,**Directorate of Secondary and Higher Secondary, Ministry of Education, Bangladesh.

,*Institute of Environmental Science, Rajshahi University, Bangladesh.

***Department of Tourism and Hospitality Management, Noakhali Science and Technology University, Bangladesh.

Ushaa Eswaran* , J. Suman, Jaishee*, Manisha**, Jeevitha*, Kowshika, Kesavan***