An effective simulation mechanism for construction operations

This paper addresses processing of spectrophotometric array signals based on genetic algorithms (GA) least square support vector machines (LS-SVM) regression to provide a powerful model for machine learning and data mining. The key to complete LS-SVM regression is to choose its optimal parameters. Due to their outstanding ability in solving global optimization problems in complex multidimensional search space, GA are used in this study to obtain the optimal parameter combination of the LS-SVM model. Experimental results showed the GA-LS-SVM method to be successful for simultaneous multicomponent determination even where severe overlap of spectra was present. KeywordsLeast squares support vector machines; Genetic algorithms; Spectrophotometric array signals; Overlapping spectra; Artificial intelligence Nowadays, with the application of photometric diode array detector and computers, rapid scanning commercial spectrophotometers are capable of quickly generating huge data consisting of hundreds and even thousands of absorbance values per spectrum. The array data named fullspectrum contain sufficient information to be able to determine the contents of various compounds. The main drawback of ultraviolet-visible (UV-VIS) is its poor selectivity because in many cases UV-VIS spectra display strong overlaps, especially some less specific and selective chromagenic reagents often give rise to strongly overlapped spectra in many cases. The combination of artificial intelligence methods with the computer-controlled spectrophotometers was proven to be effective in overcoming this difficulty [1-3]. Artificial neural network (ANN) is a form of artificial intelligence that mathematically simulates biological nervous system [4, 5]. However, ANN often has slow convergence, is prone to the existence of many local minima during training, and has a tendency of overfitting. Recently, a promising technology called support vector machines (SVM) has been used for classification and regression problems. SVM pioneered by Vapnik is a kind of machine learning method based on modern statistical learning theory and has notable properties including absence of local minima and high generalization ability [6, 7]. Suykens and his coworkers [8] introduced a modified version of SVM called least square SVM (LS-SVM) , which requires solving a set of linear equations instead of a quadratic programming problem and is much easier and computationally simpler than SVM. SVM and LS-SVM represent relatively recent artificial intelligence method and have found some applications in image analysis, classification and disease diagnosis etc. [9, 10]. It is worth mentioning that the success of LS-SVM model is highly dependent on the optimum choice of two parameters, the relative weight of regression error γ and the kernel width σ of radial basis function (RBF). Genetic algorithms (GA) [11, 12] introduced by John Holland are probabilistic optimization techniques based on natural evolution and genetics and Darwin’s theory of survival of the best. With their efficient and robust global search ability, GA are used to search two optimal parameters for the LS-SVM model simultaneously and automatically. The LS-SVM model then performs the regression task using these optimal parameters.

Genetic Algorithm (GA) may be attributed as method for optimizing the search tool for difficult problems based on genetics selection principle. In additions to Optimization it also serves the purpose of machine learning and for Research and development. It is analogous to biology for chromosome generation with variables such as selection, crossover and mutation together constituting genetic operations which would be applicable on a random population initially. GA aims to yield solutions for the consecutive generations. The extent of success in individual production is directly in proportion to fitness of solution which is represented by it, thereby ensuring that quality in successive generations will be better. The process is concluded once an GA is most suitable for the issues that need optimization associated with some computable system.. John Holland may be regarded as funding father of original genetic algorithm and is attributed to year 1970's as funding date. Additionally a random search method represented by Charles Darwin for a defined search space in order to effetely solve a problem. In this paper, what is genetic algorithm and its basic workflow is discussed how a genetic algorithm work and what are the process is included in this is also discussed. Further, the features and application of genetic algorithm are mentioned in the paper.

Firefly algorithm (FFA) optimization technique is a popular continuous optimization technique and also widely applied to tune controller gain values of proportional–integral–derivative (PID) controller. In the chaos-based firefly algorithm, the chaotic maps are included to improve the randomness when generating new solutions and thereby to increase the diversity of the population. It increases global search mobility for robust global optimization. The investigated power system incorporates two thermal power systems, and two areas are interconnected via tie-line. Also, this work system is equipped with a secondary (PID) controller. The gain value of the controller is optimized by implementing chaos-based firefly algorithm (CFFA) by applying step load in area 1. The effectiveness and supremacy of proposed optimization technique tuned controller performance are verified by comparing genetic algorithm (GA), particle swarm optimization (PSO) technique and firefly algorithm tuned PID controller performance in the same interconnected power system. The proposed optimization technique-based controller response is evaluated by considering time domain specifications parameters of the proposed optimization technique-based controller response. Further, effectiveness of recommended optimization technique is verified by adding nonlinearity’s [generation rate constraint (GRC) and governor dead band (GCB)] in the investigated power system with connecting unified power flow controller (UPFC) is applied, and it is a flexible alternating current transmission system (FACTS) controller family device connected parallel to the tie-line. Further, hydrogen aqua electrolyzer (HAE) energy storage unit with a fuel cell is connected in area 2 for improving the performance of the system during sudden load demand. Finally, simulation result proved that the proposed CFFA technique gives better controlled performance compared with GA, PSO and FFA technique tuned controller performance in terms of fast settled response with a minimum peak over and undershoot, damping oscillations during unexpected load demand conditions.

John Holland's work has combined what appears to be a complicated but technical contribution to giving approximate solutions for a class of difficult problems with a deepening of our understanding of the way we all do induction, of how the experience of the world modifies and improves our behavior and our decisions. Such a comprehension necessarily alters the viewpoint of the behavioral sciences. In this chapter, I want to concentrate on some of the already apparent ways in which he has altered our understanding of the complex dynamic system that constitutes the economic world. Of course, the genetic algorithm can be and has been used as a means of solving hard problems in economic analysis, as in any other field. That is, it is an aid to the analyst, and a powerful one. I want, however, to emphasize the second aspect of Holland's work, the implications of the genetic algorithm as a description of human problem-solving behavior in a complicated world. The economic world is complicated partly because it depends on the physical and biological world which governs the techniques of production. More interestingly, though, the economic world is complicated because the individuals in it are interacting through markets. It is an old observation among economists, going back to Adam Smith's observation of the invisible hand, that economic events are the results of human actions but are not necessarily an achievement of human intentions. In the murk of the economic world, individuals have to act. They have to make choices as to what they will consume, how much they will save, what goods they will produce and how they will produce them, and then what investments they will make. They make these choices with a view to their consequences, personal satisfaction today or in their future or the satisfaction of their heirs, the profits to be made now or in the future by producing goods, and the returns on their investments. These choices have an important time dimension; people and many of the things they buy, make, or sell last, and the outcomes of current decisions depend on events which will occur in the future. The life of the decisionmaker is uncertain, so is his or her health.

Even though the extremely uncertain current global business environment requires organizations to change their business processes and organizational structures to adapt to their extremely uncertain and complex environments, existing methodology and system cannot support this problem. This paper presents a comprehensive simulation and redesign system to simulate business processes and organizational structure simultaneously and derive the most process-oriented organizational structure efficiently. The methodology and system suggested in this study can predict the effects of changing business processes and organizational structures through the simulation and derive appropriate and practical organizational structures that execute current processes efficiently through the genetic algorithm. Existing business process researches have the limitations that they rarely have considered the effects of organizational structure on the performance, and this study have outstanding academic contribution in solving the problem systematically using simulation and optimization techniques.

This paper provides an overview of the tools used to conduct independent modeling and simulation of Artemis 1 separation events and highlights challenges and lessons learned across a decade of analysis. An overview of each of the tools along with the process flow and key components of maintaining independence from the Program’s baseline tool set will be provided. In addition, each of the separation analyses conducted will be discussed, making note of key models incorporated for each event and unique challenges that were addressed. The technical work described in this paper is part of an on-going technical assessment conducted by the NASA Engineering and Safety Center to develop and maintain an independent modeling and simulation capability in support of Space Launch System (SLS) and Multi-Purpose Crew Vehicle (MPCV) throughout design, verification, and flight readiness cycles. For Artemis 1, an end-to-end analysis approach was taken with events simulated from launch to re-entry. This paper will focus on the separation events, including liftoff clearance analysis, solid rocket booster (SRB) separation from Core Stage (CS), Service Module (SM) panel jettison, ICPS / Core Stage Separation, and MPCV/ ICPS separation. To assess separation clearances, the NESC has developed a high fidelity, independent 6-degree of freedom (DOF) simulation of the separation events. The simulation was developed using the Program to Optimize Simulated Trajectories II (POST2). POST2 is an industry standard flight trajectory optimization and simulation tool that is Class D compliant and follows NASA NPR 7150.2, which includes regression testing, unit testing, and configuration control of the simulation. The primary objective of the SLS separation and clearance analyses was to estimate the clearances between different vehicle components and/or ground support equipment (GSE) during separation events. The clearance is defined as the minimum distance from any point on one 3D geometry model associated with one vehicle component (or GSE) to any point on another 3D geometry model associated with a vehicle component separating from the main body. The vehicle geometry models used by the NESC to compute the separation clearances were derived from CAD models. POST2 simulation results were post processed in the Exploration Visualization Environment (EVE) tool to compute clearances, and detect recontact occurrences, if any, between the separating stages. EVE is a simulation, visualization, and analysis system designed to integrate time-based dynamics data with detailed graphical models in a full-scale virtual environment. EVE was used to simultaneously calculate more than 30 different separation clearances by “driving” detailed vehicle geometry models with position and attitude time history data obtained from the POST2 simulation. EVE displays each clearance distance visually, at any given instant of time, with a straight line connecting the two closest points on each object and records the time history of the clearance distance in a file. If the clearance reduces to zero, then EVE reports it as a recontact. To perform separation analyses, POST2 Monte Carlo analyses were performed by running 2000 dispersed cases that included over hundreds of uncertainties and dispersions for a range of simulation models including aerodynamics, propulsion, navigation sensors, control actuators, slosh and flexible body dynamics, and winds and atmosphere. Clearances were computed for each of the dispersed Monte Carlo trajectories and overall separation performance was statistically assessed. Over the past decade the POST2 simulation and EVE visualization tools have been used to perform detailed assessments of the separation events that occur during the Artemis 1 ascent. To support these assessments, numerous unique simulation models were developed and integrated into the standard suite of POST2 simulation models to address and resolve key concerns specific to each of the Artemis 1 separation events. In addition to presenting an overview of the multi-body 6-DOF simulation and separation analysis capability, the paper will discuss the integration of key simulation models such as flexible body dynamics, slosh dynamics, separation mechanisms, multi-body aerodynamics, and environmental models. Additional details are provided about other challenges that were addressed, including the use of a convex hull to reduce computational time for clearance calculations, the modeling of incidental recontact during separation events, and the capability to simulate failure scenarios.

The power balance is considered as most remarkable issue in power generation system. In this proposed work thermal power systems are connected through tie-line. Thermal power system is designed by considering reheat turbine with single stage, governor, and speed regulator unit. Proportional-Integral-Derivative (PID) controller is implemented to regulate the system operation. In this proposed work, the automatic generation control of three-area interconnected reheat thermal power generating system designed and discussed. The PID controller gain values are tuned with the help of more powerful evolutionary Bat Algorithm (BA) procedure. The proposed Bat algorithm tuned controller response was examined by comparing its performance with other optimization technique, namely Genetic Algorithm (GA) and Particle Swam Optimization (PSO) technique tuned controller response. Additionally, different cost functions-based bat algorithm optimized controller responses are presented and compared. The time domain specification parameters are considered for verifying the better-cost function for designing of controller. The simulated responses evident that proposed bat algorithm tuned controller output yield superior performance over GA & PSO tuned controller. Integral Time Square Error (ITSE) cost function-based BA tuned controller give better controlled response during sudden load demand condition in interconnected power system.

In mining and construction operations, the protection, safety and machinery's lifetime hold a crucial concern that can impose unwelcoming costs on the projects. The motivation behind this work is to deliver a model capable of addressing these apprehensions besides managing the potential risks and costs of the types of machinery. The presented model in this article aims to increase the quality and reliability of the products and their operations by utilizing sensor information for real-time prediction and categorization of drilling operations. This model works based on the time analyses on the sensory fused data. We applied the model on the three-axis acceleration and angular velocity signals (generated from a simulated system) to extract features and categorize three different rock drilling operations. For each operation, we measured the Median Absolute Deviation (MAD) and dynamic range parameters of the acceleration signals. In addition, we succeeded to calculate the Root Mean Square (RMS) parameter as a feature from angular velocity signals. The obtained results in this study approve the real-time prediction and categorization potential of the introduced approach for the different rock drilling operations. However, the limitation of this work can be the source of the data which is originating from the simulated normal operations. As an extending future work in future publications, we will include the faulty operation data, the real data from measurements and present data analysis of abnormal operations.

A study on flowshop scheduling problem combining Taguchi experimental design and genetic algorithm

This study aims to optimize the stock market forecast model using the Support Vector Machine. Two types of SVM optimization techniques were tested, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), in order to find which optimization technique provides the most appropriate parameters for the SVM Dot Kernel and results in improved stock market forecast efficiency. The experiment uses data from five stocks from the Stock Exchange of Thailand, which are tested and compared with results from before and after application of the optimization techniques. In addition, the SVM model, which was enhanced by the optimization technique, was backtested for trading in order to analyze the return on investment by comparing trading results with technical analysis techniques that are popular with general investors, such as Relative Strength Index (RSI), Stochastic Oscillator (STO), and Moving Average Convergence/Divergence (MACD). The results showed that genetic algorithm can improve the SVM Model for increased forecasting accuracy, and the average return results were higher than the compared results from the technical analysis indicators. In addition, this study also analyzes the SVM model trading performance based on price trends, which include uptrend, downtrend and sideways trend. This concludes that SVM with Dot Kernel optimized by GA is able to provide effective forecasting for all trends, with the best performance during the uptrend.

This paper describes the application of an object-oriented (OO) modelling concept and OO programming mechanisms to develop an activity object-oriented (AOO) simulation strategy for modelling construction operations. The AOO simulation strategy considers construction activities to be objects, thus overcoming some pitfalls due to the general simulation strategies in developing simulation models or controlling simulation experiments. This paper also discusses the characteristics and related problems of some simulation strategies used for construction simulation. Moreover, the AOO graphical modelling interface associated with the AOO simulation strategy is described. Finally, the AOO simulation system has been compared with other simulation tools.

Shear walls are commonly used in reinforced concrete buildings to enhance lateral stiffness and seismic resistance. However, in mid- to high-rise structures, their effectiveness diminishes in upper stories due to the concentration of bending demands and deformation, particularly when shear walls are curtailed below full height. This configuration can lead to excessive displacements in upper floors during strong earthquakes. To address this issue, this study investigates the integration of buckling-restrained braces (BRBs) in the upper frames of buildings with curtailed shear walls to improve seismic performance. The objective is to identify the optimal BRB placement strategy to mitigate upper-story deformations while maintaining structural efficiency. Numerical modeling was conducted on mid- and high-rise reinforced concrete frames incorporating partial shear walls, and seismic analysis was carried out using nonlinear dynamic simulations. Two metaheuristic optimization techniques, namely Genetic Algorithm (GA) and Harmony Search Algorithm (HSA), were applied to determine the most effective BRB configurations, as they are well-suited for solving complex, nonlinear structural optimization problems. The results indicate that BRB placement significantly enhances the structural response under seismic loading, particularly in the upper stories where shear walls are absent. Among the two algorithms, GA demonstrated greater computational efficiency and effectiveness in identifying optimal brace locations. These findings support the use of BRBs as a supplemental system in buildings with curtailed shear walls and highlight the potential of evolutionary algorithms in optimizing seismic design solutions.

Some comparisons for damage detection on structures using genetic algorithms and modal sensitivity method

Software simulation is one of the most common methods to study worm propagation. However, simulation process and software interface, which lack unified description, cause inefficiencies of worm propagation simulation. In order to achieve a unified description for worm propagation simulation process, we propose a Worm Propagation Simulation Description Language (WPSDL), which characterizes the software interfaces and parameters of each module in simulation process in a unified way. Meanwhile, we develop a simulation system of worm propagation oriented our presented language. In this system, we use related commands of the description language as interactive instructions, and then implement worm propagation simulation for different types of network worms. In addition, we conduct a series of experiments to analyse the impacts of worm propagation mechanism and network topology structure. The experiments show that increasing infection ability and scanning rate can improve the propagation scale significantly, and the propagation delay can influence the outbreak time and explosion intensity of worm propagation. The experiments not only provide support for the study of worm propagation mechanisms, but also verify the practicability of the description language and the simulation system.

This paper adopts the High Level Architecture to develop the space-based laser active imaging distribution simulation software system, and designs the system framework which contains three-step workflow including modeling, experimental and analysis. The paper puts forward the general needs of the simulation system first, then builds the simulation system architecture based on HLA and constructs 7 simulation federal members. The simulation system has the primary functions of space target scattering characteristic analysis, imaging simulation, image processing and target recognition, and system performance analysis and so on, and can support the whole simulation process. The results show that the distribution simulation system can meet the technical requirements of the space-based laser imaging simulation.