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According the need of Artificial intelligence in every field and all area the development takes speed. The area of civil engineering, virtual reality (VR) technology's use and developed fast and also used in many areas in civil engineering. The design and construction phases of civil engineering projects must be as efficient and error-free as possible since they are intricate and expensive process of work convert fast to implement. Engineers will be able to visualise and test many situations before actually creating them, so the use of virtual reality (VR) technology, which help to identify the revolution and correction work with correct parameters. Todays need VR technology in civil engineering and highlights some of its key features, including its ability to improve collaboration, reduce project time and costs, and enhance safety. The paper also discusses the challenges associated with the implementation of VR technology in civil engineering, such as the need for specialized hardware and software, training, and data management. To demonstrate the practical application of VR technology in civil engineering, the paper presents case studies from real-world projects. These case studies showcase the use of VR technology in different stages of the construction process, including design, project planning, and construction management. The application of VR technology in civil engineering has great potential to improve project outcomes and increase efficiency. However, it is crucial to address the challenges associated with its implementation to ensure successful integration into the industry. Keyword: Virtual Reality Technology, Hardware, Software, Training, Data Management, Civil Engineering.

Structural damage recognition is a difficult task for the field of civil engineering, as it can help prevent catastrophic failures of infrastructure. The traditional approach to damage recognition involves visual inspection by human experts, which can be time-consuming, expensive, and subjective. In recent years, artificial intelligence (AI) has been gaining attention in the field of structural damage recognition, with deep transfer learning being a promising technique. This research paper investigates the use of deep transfer learning for image-based structural damage recognition and proposes a methodology for its implementation. The process to performance of different deep transfer learning models and identifies the key factors that influence their effectiveness. The method use by AI model involves the use of pre-trained deep learning models, fine-tuning, and transfer learning to improve the accuracy of structural damage recognition. The deep transfer learning can significantly improve the accuracy and efficiency of image-based structural damage recognition and recommends the adoption of the proposed methodology in the field of civil engineering. Artificial intelligence technology has advanced quickly, particularly machine learning algorithms, which have achieved outstanding outcomes in a number of industries while also garnering attention. Artificial intelligence technology has advanced quickly. The study on damage detection and artificial intelligence approaches is detailed in this publication. Sensing data is the starting point for several Image structure processing AI techniques. The quality of the subsequent processing techniques directly depends on the strengths and weaknesses of the sensing data itself. Since there are many different forms of sensing data, this study divides damage detection investigations into a few groups based on those types: visual image, point cloud, infrared thermal imaging, vibration response, and other sorts of data.

International Journal of Advanced Research in Engineering and Technology (IJARET) Volume 11, Issue 2, February 2020, pp. 502-511, Article ID: IJARET_11_02_049 Available online at https://iaeme.com/Home/issue/IJARET?Volume=11&Issue=2 ISSN Print: 0976-6480 and ISSN Online: 0976-6499 INVESTIGATE THE COMBINED USE OF SIEVED SUGARCANE BAGASSE ASH AND MARBLE WASTE WITH OPTIMUM PROPORTION IN CONCRETE Mahesh Chandra Shah Department of Civil Engineering, Graphic Era Hill University, Dehradun, Uttarakhand, India 248002 Abstract: Ash from sugarcane is a waste product. Sugarcane ash and marble trash are only two examples of the industrial waste that is a major source of pollution and health problems today. This article discusses the use of industrial byproducts as a cementitious additive in masonry mortar, namely Sugarcane Bagasse Ash (SCBA) and Marble Slurry Dust (MSD). Finely ground sugarcane bagasse ash (SCBA) and Marble Slurry Dust (MSD) were used to replace varying percentages of Portland cement (OPC) in the mix. The replacement percentages ranged from 0% to 20%. By assessing the compressive strength of cubes at 7, 21, and 28 days of curing age and comparing the findings to those of a control specimen, This study showed that sugarcane bagasse ash and Marble Slurry Dust improved the durability of mortar. The research concluded that up to 10% sugarcane bagasse ash and 10% marble slurry dust are both effective pozzolanic additives in mortar. Key words: Sugarcane Bagasse Ash (SCBA), Marble Slurry Dust (MSD), pozzolanic material, Compressive strength, Industrial waste Cite this Article: Mahesh Chandra Shah, Investigate the combined use of sieved sugarcane bagasse ash and marble waste with optimum proportion in concrete, International Journal of Advanced Research in Engineering and Technology, 11(2), 2020, pp. 502-511. DOI: https://doi.org/10.17605/OSF.IO/TD3HW

The development of efficient and cost-effective concrete formwork systems is of paramount importance in the construction industry. Traditionally, the design and optimization of formwork systems have relied heavily on physical prototypes and empirical methods. However, these approaches are time-consuming, expensive, and often lack the necessary flexibility to accommodate various design parameters. In recent years, computational modelling techniques, such as the Discrete Element Method (DEM), have emerged as powerful tools for simulating the behaviour of granular materials, including concrete. This research aims to contribute to the advancement of concrete formwork system design by developing a simulation framework using the Discrete Element Method. The proposed simulation model incorporates the discrete behaviour of individual concrete particles and their interactions, enabling the analysis of complex formwork systems in a virtual environment. By simulating the behaviour of concrete during the pouring, compaction, and curing processes, the model can provide valuable insights into the structural integrity, stability, and performance of formwork systems. The simulation framework is implemented using computational tools and algorithms capable of accurately representing the physical behaviour of concrete. By considering various factors, such as particle shape, size distribution, inter-particle friction, and boundary conditions, the model can mimic the real-world behaviour of concrete formwork systems with high fidelity. The simulation outputs include measurements of stress distribution, deformation, and potential failure modes, allowing engineers and designers to optimize formwork systems and enhance their performance. The development of a simulation of concrete formwork systems using the Discrete Element Method offers numerous benefits. It enables virtual prototyping and testing of formwork designs, reducing the reliance on physical prototypes and associated costs. The ability to analyse and visualize the behaviour of concrete under different loading and environmental conditions facilitates the identification of potential design flaws and optimization opportunities. Moreover, the simulation model can aid in improving the safety, efficiency, and sustainability of formwork systems by guiding the selection of appropriate materials, construction techniques, and structural configurations. The simulation framework based on the Discrete Element Method for the analysis and optimization of concrete formwork systems. By accurately capturing the behaviour of concrete particles and their interactions, the simulation model provides valuable insights into the performance and stability of formwork systems. This approach has the potential to revolutionize the design process, leading to the development of more efficient, cost-effective, and sustainable formwork systems in the construction industry.

International Journal of Advanced Research in Engineering and Technology (IJARET) Volume 11, Issue 2, February 2020, pp. 449-459, Article ID: IJARET_11_02_044 Available online at https://iaeme.com/Home/issue/IJARET?Volume=11&Issue=2 ISSN Print: 0976-6480 and ISSN Online: 0976-6499 STUDY THE SUITABILITY OF WIRE MESH AND CFRP WOUNDED OVER WIRE MESH IN THE FERROCEMENT SLABS Mahesh Chandra Shah Department of Civil Engineering, Graphic Era Hill University, Dehradun, Uttarakhand, India 248002 Abstract: Part of a series of experimental reports examining the viability of using ferrocement panels as a long-term replacement for reinforced concrete slabs with lightweight blocks infill, this study presents preliminary findings from those investigations. The ferrocement panels used are a strain-hardening kind of HPFRCC made possible by the use of polyvinyl alcohol fiber. Steel plate, Glass and Carbon Fibre Reinforced Polymers, and other materials have all been tried by researchers in an effort to boost the strength of reinforced concrete beams. Due to their high weight in relation to their strength and their incompatibility in strength with steel bars, steel plates, GFRP, and CFRP composites are seldom used in practice for reinforcing works. The results showed that by using the ideal fiber/wire mesh content, the flexure strength of the hybrid ferrocement panels was significantly enhanced in comparison to the flexure strength of the ferrocement utilized in the control group. Key words: Wire mesh, CFRP, Hybrid polyvinyl alcohol (PVA) fiber, Reinforced concrete. Cite this Article: Mahesh Chandra Shah, Study the suitability of wire mesh and CFRP wounded over wire mesh in the ferrocement slabs, International Journal of Advanced Research in Engineering and Technology, 11(2), 2020, pp. 449-459. DOI: https://doi.org/10.17605/OSF.IO/XT4UZ

International Journal of Civil Engineering and Technology (IJCIET) Volume 10, Issue 1, January 2019, pp. 3248-3263, Article ID: IJCIET_10_01_278 Available online at https://iaeme.com/Home/issue/IJCIET?Volume=10&Issue=1 ISSN Print: 0976-6308 and ISSN Online: 0976-6316 MACHINE LEARNING ALGORITHM USED FOR DEVELOPMENT OF FORECASTING LANDSLIDE SUSCEPTIBILITY Mahesh Chandra Shah Department of Civil Engineering, Graphic Era Hill University, Dehradun, Uttarakhand, India 248002 Cite this Article: Mahesh Chandra Shah, Machine learning algorithm used for development of forecasting landslide susceptibility, International Journal of Civil Engineering and Technology, 10(1), 2019, pp. 3248-3263. DOI: https://doi.org/10.17605/OSF.IO/XPUY2

The critical aspect of foundation design for soil, particularly in deep foundations where the load capacity and settlement behaviour are heavily influenced by the relationship between the soil and the framework of construction, Analysis of dynamic and static SSI in deep foundations, using both analytical and numerical methods. We review the current state-of-the-art techniques for modelling and analysing SSI. Our analysis shows that dynamic and static SSI can have significant effects on the behaviour of deep foundations and that proper consideration of SSI is essential for ensuring the safety and performance of these structures. The analysis starts with a review of the fundamental concepts of SSI. The study then focuses on the analysis of dynamic SSI simulate the response of the soil and structure to dynamic loads. Presents a study on static SSI, which involves the use of analytical methods to evaluate behaviour of deep foundations under static loads. The analysis considers a range of factors, including soil properties, foundation geometry, and loading conditions. Finally, the paper concludes with a discussion of the findings and their implications for foundation design and construction. The study shows that the behaviour of deep foundations under dynamic and static loads is highly dependent on the SSI and that accurate modelling and analysis of this interaction are critical for ensuring the safe and efficient design of deep foundations.