Predicting Shear Capacity of RC Beams Strengthened with NSM FRP Using Neural Networks

This research aims to predict the shear capacity of NSM FRP beams using the neural network method. The study investigates the key considerations and the necessary analysis for this prediction. NSM FRP beams are reinforced concrete beams that are strengthened with near-surface mounted (NSM) fiber-reinforced polymer (FRP) composites. Accurately predicting their shear capacity is important for ensuring their safety and reliability in real-world applications. The neural network method is a machine learning approach that is increasingly used in engineering analysis and design. The study explores how this method can be used to predict the shear capacity of NSM FRP beams and what factors should be taken into account in this analysis. The research also discusses the analytical approach required for this prediction, highlighting the necessary steps for obtaining accurate results. Overall, this study provides valuable insights into the use of the neural network method for predicting the shear capacity of NSM FRP beams. The findings can help inform future research and practical applications in the field of structural engineering, contributing to the development of safer and more reliable structures

Data-Driven Analysis Of Construction Bidding Stage-Related Causes Of Disputes

Construction bidding is a complex process that involves several potential risks and uncertainties for all the stakeholders involved. Such complexities, risks, and uncertainties, if uncontrolled, can lead to the rise of claims, conflicts, and disputes during the course of a project. Even though a substantial amount of knowledge has been acquired about construction disputes and their causation, there is a lack of research that examines the causes of disputes associated with the bidding phase of projects. This study addresses this knowledge gap within the context of infrastructure projects. In investigating and analyzing the causation of disputes related to the bidding stage, the authors implemented a multistep research methodology that incorporated data collection, network analysis (NA), spectral clustering, and association rule analysis (ARA). Based on a manual content analysis of 94 legal cases, the authors identified a comprehensive list of 27 causes of disputes associated with the bidding stage of infrastructure projects. The NA results indicated that the major common causes leading to disputes in infrastructure projects comprise inaccurate cost estimates, inappropriate tender documents, nonproper or untimely notification of errors in a submitted bid, nonproper or untimely notification of errors in tender documents, and noncompliance with Request for Proposals\u27 (RFP) requirements. Upon categorizing and clustering the causes of disputes, the ARA results revealed that the most critical associations are related to differing site conditions, errors in submitted bids, unbalanced bidding, errors in cost estimates, and errors in tender documents. This study promotes an in-depth understanding of the causes of disputes associated with the bidding phase within the context of infrastructure projects, which should better enable the establishment of proactive plans and practices to control these causes as well as mitigate the occurrence of their associated disputes during project execution

FSM-F: finite state machine based framework for denial of service and intrusion detection in manet

Due to the continuous advancements in wireless communication in terms of quality of communication and affordability of the technology, the application area of Mobile Adhoc Networks (MANETs) significantly growing particularly in military and disaster management. Considering the sensitivity of the application areas, security in terms of detection of Denial of Service (DoS) and intrusion has become prime concern in research and development in the area. The security systems suggested in the past has state recognition problem where the system is not able to accurately identify the actual state of the network nodes due to the absence of clear definition of states of the nodes. In this context, this paper proposes a framework based on Finite State Machine (FSM) for denial of service and intrusion detection in MANETs. In particular, an Interruption Detection system for Adhoc On-demand Distance Vector (ID-AODV) protocol is presented based on finite state machine. The packet dropping and sequence number attacks are closely investigated and detection systems for both types of attacks are designed. The major functional modules of ID-AODV includes network monitoring system, finite state machine and attack detection model. Simulations are carried out in network simulator NS-2 to evaluate the performance of the proposed framework. A comparative evaluation of the performance is also performed with the state-of-theart techniques: RIDAN and AODV. The performance evaluations attest the benefits of proposed framework in terms of providing better security for denial of service and intrusion detection attacks

F3TM: flooding factor based trust management framework for secure data transmission in MANETs

Due to the absence of infrastructure support, secure data dissemination is a challenging task in scalable mobile ad hoc networks (MANETs) environment. In most of the traditional routing techniques for MANETs, either security has not been taken into account or only one aspect of security concern has been addressed without optimizing the routing performance. This paper proposes Flooding Factor based Framework for Trust Management (F3TM) in MANETs. True flooding approach is utilized to identify attacker nodes based on the calculation of trust value. Route Discovery Algorithm is developed to discover an efficient and secure path for data forwarding using Experimental Grey Wolf algorithm for validating network nodes. Enhanced Multi-Swarm Optimization is used to optimize the identified delivery path. Simulations are carried out in ns2 to assess and compare the performance of F3TM with the state-of-the-art frameworks: CORMAN and PRIME considering the metrics including delay, packet delivery ration, overhead and throughput. The performance assessment attests the reliable security of F3TM compared to the state-of-the-art frameworks

Numerical and experimental investigations on efficient design and performance of hydrokinetic Banki cross flow turbine for rural areas

Micro hydrokinetic energy scheme presents an attractive, environmentally-friendly and efficient electric generation in rural, remote and hilly areas. However, this scheme is yet to be fully discovered, as researchers are still searching for solutions for the main problems of low velocity of current in the open flow channels and low efficiency of hydrokinetic turbines. This research proposes a novel system configuration to capture as much kinetic energy as possible from stream water current. This system, known as bidirectional diffuser augmented (BDA) channel, functions by utilizing dual directed nozzles in the flow and is surrounded by dual cross flow/Banki turbines. It is also important to obtain the efficient design parameters of the turbines to use in the current configuration. The appropriate angle is important in order to guide the flow to touch the blades more perpendicularly to capture as much torque and power as possible. Hence, experimental and numerical investigations have been carried out in this research paper to study the performance characteristics of the CFT configuration applied in BDA system and investigate the effects of blades’ inlet and outlet angles of CFT runners on the internal flow characteristics and efficiency. In this study, four different runners with various inlet and outlet angles of two CFT have been investigated. The CFD results have been validated with the experimental work and proven acceptable with flow pattern and performance characteristics. The results of the current study conclude that the maximum power coefficients (Cp) of 0.612 and 0.473 for lower and upper turbines are recorded for best runner angles of Case 3