An introduction of Krill Herd algorithm for engineering optimization

A new metaheuristic optimization algorithm, called Krill Herd (KH), has been recently proposed by Gandomi and Alavi (2012). In this study, KH is introduced for solving engineering optimization problems. For more verification, KH is applied to six design problems reported in the literature. Further, the performance of the KH algorithm is com­pared with that of various algorithms representative of the state-of-the-art in the area. The comparisons show that the results obtained by KH are better than the best solutions obtained by the existing methods. First published online: 25 Aug 201

Seismic Failure Probability and Vulnerability Assessment of Steel-Concrete Composite Structures

Building collapse in earthquakes caused huge losses, both in human and economic terms. To assess the risk posed by using the composite members, this paper investigates seismic failure probability and vulnerability assessment of steel-concrete composite structures constituted by rectangular concrete filled steel tube (RCFT) columns and steel beams. To enable numerical simulation of RCFT-structure, the details of components modeling are developed using OpenSEES finite element analysis package and the validation of proposed procedure is investigated through comparisons with available experimental results. The seismic fragility and vulnerability curves of RCFT-structures are created through nonlinear dynamic analysis using an appropriate suite of ground motions for seismic loss assessment. These curves developed for three-, six- and nine-story prototypes of RCFT-structure. Fragility curves are an appropriate tool for representing the seismic failure probabilities and vulnerability curves demonstrate a probability of exceeding loss to a measure of ground motion intensity

Gene expression programming approach to cost estimation formulation for utility projects

This article utilizes gene expression programming (GEP) technique to develop a prediction model in order to automate estimating the construction cost of water and sewer replacement/rehabilitation projects. A database gathered for developing the model was established on the basis of data related to 210 actual water and sewer projects obtained from the City of San Diego, California, USA. To verify the predictability of the GEP model, it was examined to estimate the cost of the projects that were not included in the modelling process. Sensitivity analysis technique and professional experiences were employed to determine the contributions of the qualitative factors and quantifiable parameters affecting the cost estimate. The proposed model with correlation coefficient of 0.8467 is adequately capable of estimating the cost of water and sewer replacement/rehabilitation projects. The GEP-based design equation can easily be used for predesign purposes to help allocate budgets and available limited resources effectively

Risk analysis of BOT contracts using soft computing

Build-Operate-Transfer (BOT) contracts have been widely implemented in developing countries facing budget constraints. Analysing the expected variability in project viability requires extensive risk analysis. An objective analysis of various risk variables and their influence on a BOT project evaluation requires study and integration of many sce­narios into the concession terms, which is complicated and time-consuming. If the process of negotiating the financial parameters and uncertainties of a BOT project could be automated, this would be a milestone in objective decision-mak­ing from various stakeholders’ points of view. A soft computing model would let the user incorporate as many scenarios as could be provided. Extensive risk analysis could then be easily performed, leading to more accurate and dependable results. In this research, an artificial neural network model with correlation coefficient of 0.9064 has been used to model the relationship between important project parameters and risk variables. This information was extracted from sensitiv­ity analysis and Monte Carlo simulation results obtained from conventional spreadsheet data. The resulting consensus would yield to fair contractual agreements for both the government and the concession company. First published online: 01 Jul 201

A quantum-inspired sensor consolidation measurement approach for cyber-physical systems.

Cyber-Physical System (CPS) devices interconnect to grab data over a common platform from industrial applications. Maintaining immense data and making instant decision analysis by selecting a feasible node to meet latency constraints is challenging. To address this issue, we design a quantum-inspired online node consolidation (QONC) algorithm based on a time-sensitive measurement reinforcement system for making decisions to evaluate the feasible node, ensuring reliable service and deploying the node at the appropriate position for accurate data computation and communication. We design the Angular-based node position analysis method to localize the node through rotation and t-gate to mitigate latency and enhance system performance. We formalize the estimation and selection of the feasible node based on quantum formalization node parameters (node contiguity, node optimal knack rate, node heterogeneity, probability of fusion variance error ratio). We design a fitness function to assess the probability of node fitness before selection. The simulation results convince us that our approach achieves an effective measurement rate of performance index by reducing the average error ratio from 0.17-0.22, increasing the average coverage ratio from 29% to 42%, and the qualitative execution frequency of services. Moreover, the proposed model achieves a 74.3% offloading reduction accuracy and a 70.2% service reliability rate compared to state-of-the-art approaches. Our system is scalable and efficient under numerous simulation frameworks

A Comprehensive Bibliometric Analysis on Social Network Anonymization: Current Approaches and Future Directions

In recent decades, social network anonymization has become a crucial research field due to its pivotal role in preserving users' privacy. However, the high diversity of approaches introduced in relevant studies poses a challenge to gaining a profound understanding of the field. In response to this, the current study presents an exhaustive and well-structured bibliometric analysis of the social network anonymization field. To begin our research, related studies from the period of 2007-2022 were collected from the Scopus Database then pre-processed. Following this, the VOSviewer was used to visualize the network of authors' keywords. Subsequently, extensive statistical and network analyses were performed to identify the most prominent keywords and trending topics. Additionally, the application of co-word analysis through SciMAT and the Alluvial diagram allowed us to explore the themes of social network anonymization and scrutinize their evolution over time. These analyses culminated in an innovative taxonomy of the existing approaches and anticipation of potential trends in this domain. To the best of our knowledge, this is the first bibliometric analysis in the social network anonymization field, which offers a deeper understanding of the current state and an insightful roadmap for future research in this domain.Comment: 73 pages, 28 figure

Residual Energy Based Cluster-head Selection in WSNs for IoT Application

Wireless sensor networks (WSN) groups specialized transducers that provide sensing services to Internet of Things (IoT) devices with limited energy and storage resources. Since replacement or recharging of batteries in sensor nodes is almost impossible, power consumption becomes one of the crucial design issues in WSN. Clustering algorithm plays an important role in power conservation for the energy constrained network. Choosing a cluster head can appropriately balance the load in the network thereby reducing energy consumption and enhancing lifetime. The paper focuses on an efficient cluster head election scheme that rotates the cluster head position among the nodes with higher energy level as compared to other. The algorithm considers initial energy, residual energy and an optimum value of cluster heads to elect the next group of cluster heads for the network that suits for IoT applications such as environmental monitoring, smart cities, and systems. Simulation analysis shows the modified version performs better than the LEACH protocol by enhancing the throughput by 60%, lifetime by 66%, and residual energy by 64%

A Multi-Criteria Decision-Making Approach for Selection of Wave Energy Converter Optimal Site

Ocean wave energy is an essential source of renewable power for coastal communities. Choosing the optimal site for the wave energy converter (WEC) deployment depends on a number of criteria. The characteristics of the WEC must be taken into account in the prediction power supply, whereas the local sea state is connected to elements like wave condition (as a representation of construction budget) and energy output as well as the influence of the exploitable storage of wave energy and its trend. As a result, this research provides a multi-criteria decision-making (MCDM) strategy for considering several factors simultaneously to choose the best possible site. The suggested MCDM technique incorporates two primary factors, i.e., exploitable storage of wave energy and energy production, into a single metric that takes into account both WEC efficiency of a particular type, WEPTOS, and sea state to aid decision-makers in the development of a pilot project. The method was then used to analyse the waves at two locations that had been identified as promising sites for harvesting wave energy along the coast of Oman. To further assess a site's potential upcoming pilot project and select the most efficient WEC, we compared the MCDM results at the stations with certain WEC types. In conclusion, optimal sites for placement of the WEPTOS WEC along the coast of Oman were identified considering the highest annual energy production and exploitable energy storage. Through solving the MCDM technique, 17 sites were pinpointed, and only 6 points were picked up

New design equations for elastic modulus of concrete using multi expression programming

An innovative multi expression programming (MEP) approach is used to derive new predictive equations for tangent elastic modulus of normal strength concrete (NSC) and high strength concrete (HSC). Similar to several building codes, the modulus of elasticity of NSC and HSC is formulated in terms of concrete compressive strength. Furthermore, a generic model is developed for the estimation of the elastic modulus of both NSC and HSC. Comprehensive databases are gathered from the literature to develop the models. For more verification, a parametric analysis is carried out and discussed. The proposed formulas are found to be accurate for the prediction of the elastic modulus of NSC and HSC. The predictions made by the MEP-based models are more accurate than those obtained by the existing models