In this paper a structural optimization technique based on a modified genetic algorithm (GA) is presented. The technique is developed to deal with discrete design optimization of…
Abstract
In this paper a structural optimization technique based on a modified genetic algorithm (GA) is presented. The technique is developed to deal with discrete design optimization of structural steelwork. Also, the paper discusses the effect of different approaches, employed for the determination of the effective buckling length of a column, on the optimum design. In order to consider realistic steelwork design problems, a modified GA has been linked to a system of structural design rules (British Standards BS 5950 and BS 6399), interacting with a finite element package. In the formulation of the optimization problem, the objective function is the total weight of the structural members, as it gives a reasonably accurate estimation of the cost. The cross‐sectional properties of the structural members, which form the set of design variables, are chosen from two separate catalogues (universal beams and columns) covered by the British Standard BS 4. The minimum weight designs of two plane steel frame structures subjected to realistic multiple loading cases are obtained. These examples show that the modified GA in combination with structural design rules and more accurate analysis provides an efficient tool for practicing designers of steel frame structures. Finally, it is shown that the resulting design optimization is considerably influenced by a specific choice of a technique employed for the evaluation of the effective buckling length of structural members.
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The fishing cat's unique hunting strategies, including ambush, detection, diving and trapping, inspired the development of a novel metaheuristic optimization algorithm named the…
Abstract
Purpose
The fishing cat's unique hunting strategies, including ambush, detection, diving and trapping, inspired the development of a novel metaheuristic optimization algorithm named the Fishing Cat Optimizer (FCO). The purpose of this paper is to introduce FCO, offering a fresh perspective on metaheuristic optimization and demonstrating its potential for solving complex problems.
Design/methodology/approach
The FCO algorithm structures the optimization process into four distinct phases. Each phase incorporates a tailored search strategy to enrich the diversity of the search population and attain an optimal balance between extensive global exploration and focused local exploitation.
Findings
To assess the efficacy of the FCO algorithm, we conducted a comparative analysis with state-of-the-art algorithms, including COA, WOA, HHO, SMA, DO and ARO, using a test suite comprising 75 benchmark functions. The findings indicate that the FCO algorithm achieved optimal results on 88% of the test functions, whereas the SMA algorithm, which ranked second, excelled on only 21% of the functions. Furthermore, FCO secured an average ranking of 1.2 across the four benchmark sets of CEC2005, CEC2017, CEC2019 and CEC2022, demonstrating its superior convergence capability and robustness compared to other comparable algorithms.
Research limitations/implications
Although the FCO algorithm performs excellently in solving single-objective optimization problems and constrained optimization problems, it also has some shortcomings and defects. First, the structure of the FCO algorithm is relatively complex and there are many parameters. The value of parameters has a certain impact on solving optimization problems. Second, the computational complexity of the FCO algorithm is relatively high. When solving high-dimensional optimization problems, it takes more time than algorithms such as GWO and WOA. Third, although the FCO algorithm performs excellently in solving multimodal functions, it rarely obtains the theoretical optimal solution when solving combinatorial optimization problems.
Practical implications
The FCO algorithm is applied to the solution process of five common engineering design optimization problems.
Originality/value
This paper innovatively proposes the FCO algorithm, which mimics the unique hunting mechanisms of fishing cats, including strategies such as lurking, perceiving, rapid diving and precise trapping. These mechanisms are abstracted into four closely connected iterative stages, corresponding to extensive and in-depth exploration, multi-dimensional fine detection, rapid and precise developmental search and localized refinement and contraction search. This enables efficient global optimization and local fine-tuning in complex environments, significantly enhancing the algorithm's adaptability and search efficiency.
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Dianzi Liu, Chengyang Liu, Chuanwei Zhang, Chao Xu, Ziliang Du and Zhiqiang Wan
In real-world cases, it is common to encounter mixed discrete-continuous problems where some or all of the variables may take only discrete values. To solve these non-linear…
Abstract
Purpose
In real-world cases, it is common to encounter mixed discrete-continuous problems where some or all of the variables may take only discrete values. To solve these non-linear optimization problems, the use of finite element methods is very time-consuming. The purpose of this study is to investigate the efficiency of the proposed hybrid algorithms for the mixed discrete-continuous optimization and compare it with the performance of genetic algorithms (GAs).
Design/methodology/approach
In this paper, the enhanced multipoint approximation method (MAM) is used to reduce the original nonlinear optimization problem to a sequence of approximations. Then, the sequential quadratic programing technique is applied to find the continuous solution. Following that, the implementation of discrete capability into the MAM is developed to solve the mixed discrete-continuous optimization problems.
Findings
The efficiency and rate of convergence of the developed hybrid algorithms outperforming GA are examined by six detailed case studies in the ten-bar planar truss problem, and the superiority of the Hooke–Jeeves assisted MAM algorithm over the other two hybrid algorithms and GAs is concluded.
Originality/value
The authors propose three efficient hybrid algorithms, the rounding-off, the coordinate search and the Hooke–Jeeves search-assisted MAMs, to solve nonlinear mixed discrete-continuous optimization problems. Implementations include the development of new procedures for sampling discrete points, the modification of the trust region adaptation strategy and strategies for solving mix optimization problems. To improve the efficiency and effectiveness of metamodel construction, regressors f defined in this paper can have the form in common with the empirical formulation of the problems in many engineering subjects.
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A. Kaveh and M. Shahrouzi
The generality of the genetic search in the light of proper coding schemes, together with its non‐gradient‐based search, has made it popular for many discrete problems including…
Abstract
Purpose
The generality of the genetic search in the light of proper coding schemes, together with its non‐gradient‐based search, has made it popular for many discrete problems including structural optimization. However, the required computational effort increases as the cardinality of the search space and the number of design variables increase. Memetic algorithms are formal attempts to reduce such a drawback for real‐world problems incorporating some kind of problem‐specific information. This paper aims to address this issue.
Design/methodology/approach
In this paper both Lamarckian and Baldwinian approaches for meme evolution are implemented using the power of graph theory in topology assessment. For this purpose, the concept of load path connectivity in frame bracing layouts is introduced and utilized by the proposed graph theoretical algorithms. As an additional search refinement tool, a dynamic mutation band control is recommended. In each case, the results are studied via a set of ultimate design family rather than one pseudo optimum. The method is further tested using a number of steel frame examples and its efficiency is compared with conventional genetic search.
Findings
Here, the problem of bracing layout optimization in steel frames is studied utilizing a number of topological guidelines.
Originality/value
The method of this paper attempts to reduce the computational effort for optimal design of real‐world problems incorporating some kind of problem‐specific information.
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Amir Hossein Alavi, Ali Mollahasani, Amir Hossein Gandomi and Jafar Boluori Bazaz
The purpose of this paper is to develop new constitutive models to predict the soil deformation moduli using multi expression programming (MEP). The soil deformation parameters…
Abstract
Purpose
The purpose of this paper is to develop new constitutive models to predict the soil deformation moduli using multi expression programming (MEP). The soil deformation parameters formulated are secant (Es) and reloading (Er) moduli.
Design/methodology/approach
MEP is a new branch of classical genetic programming. The models obtained using this method are developed upon a series of plate load tests conducted on different soil types. The best models are selected after developing and controlling several models with different combinations of the influencing parameters. The validation of the models is verified using several statistical criteria. For more verification, sensitivity and parametric analyses are carried out.
Findings
The results indicate that the proposed models give precise estimations of the soil deformation moduli. The Es prediction model provides considerably better results than the model developed for Er. The Es formulation outperforms several empirical models found in the literature. The validation phases confirm the efficiency of the models for their general application to the soil moduli estimation. In general, the derived models are suitable for fine‐grained soils.
Originality/value
These equations may be used by designers to check the general validity of the laboratory and field test results or to control the solutions developed by more in‐depth deterministic analyses.
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The cash flow from government agencies to contractors, called progress payment, is a critical step in public projects. The delays in progress payments significantly affect the…
Abstract
Purpose
The cash flow from government agencies to contractors, called progress payment, is a critical step in public projects. The delays in progress payments significantly affect the project performance of contractors and lead to conflicts between two parties in the Turkish construction industry. Although some previous studies focused on the issues in internal cash flows (e.g. inflows and outflows) of construction companies, the context of cash flows from public agencies to contractors in public projects is still unclear. Therefore, the primary objective of this study is to develop and test diverse machine learning-based predictive models on the progress payment performance of Turkish public agencies and improve the predictive performance of these models with two different optimization algorithms (e.g. first-order and second-order). In addition, this study explored the attributes that make the most significant contribution to predicting the payment performance of Turkish public agencies.
Design/methodology/approach
In total, project information of 2,319 building projects tendered by the Turkish public agencies was collected. Six different machine learning algorithms were developed and two different optimization methods were applied to achieve the best machine learning (ML) model for Turkish public agencies' cash flow performance in this study. The current research tested the effectiveness of each optimization algorithm for each ML model developed. In addition, the effect size achieved in the ML models was evaluated and ranked for each attribute, so that it is possible to observe which attributes make significant contributions to predicting the cash flow performance of Turkish public agencies.
Findings
The results show that the attributes “inflation rate” (F5; 11.2%), “consumer price index” (F6; 10.55%) and “total project duration” (T1; 10.9%) are the most significant factors affecting the progress payment performance of government agencies. While decision tree (DT) shows the best performance among ML models before optimization process, the prediction performance of models support vector machine (SVM) and genetic algorithm (GA) has been significantly improved by Broyden–Fletcher–Goldfarb–Shanno (BFGS)-based Quasi-Newton optimization algorithm by 14.3% and 18.65%, respectively, based on accuracy, AUROC (Area Under the Receiver Operating Characteristics) and F1 values.
Practical implications
The most effective ML model can be used and integrated into proactive systems in real Turkish public construction projects, which provides management of cash flow issues from public agencies to contractors and reduces conflicts between two parties.
Originality/value
The development and comparison of various predictive ML models on the progress payment performance of Turkish public owners in construction projects will be the first empirical attempt in the body of knowledge. This study has been carried out by using a high number of project information with diverse 27 attributes, which distinguishes this study in the body of knowledge. For the optimization process, a new hyper parameter tuning strategy, the Bayesian technique, was adopted for two different optimization methods. Thus, it is available to find the best predictive model to be integrated into real proactive systems in forecasting the cash flow performance of Turkish public agencies in public works projects. This study will also make novel contributions to the body of knowledge in understanding the key parameters that have a negative impact on the payment progress of public agencies.
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Riccardo Amirante and Paolo Tamburrano
The purpose of this paper is to propose an effective methodology for the industrial design of tangential inlet cyclone separators that is based on the fully three-dimensional (3D…
Abstract
Purpose
The purpose of this paper is to propose an effective methodology for the industrial design of tangential inlet cyclone separators that is based on the fully three-dimensional (3D) simulation of the flow field within the cyclone coupled with an effective genetic algorithm.
Design/methodology/approach
The proposed fully 3D computational fluid dynamics (CFD) model makes use of the Reynold stress model for the accurate prediction of turbulence, while the particle trajectories are simulated using the one-way coupling discrete phase, which is a model particularly effective in case of low concentration of dust. To validate the CFD model, the numerical predictions are compared with experimental data available in the scientific literature. Eight design parameters were chosen, with the two objectives being the minimization of the pressure drop and the maximization of the collection efficiency.
Findings
The optimization procedure allows the determination of the Pareto Front, which represents the set of the best geometries and can be instrumental in taking an optimal decision in the presence of such a trade-off between the two conflicting objectives. The comparison among the individuals belonging to the Pareto Front with a more standard cyclone geometry shows that such a CFD global search is very effective.
Practical implications
The proposed procedure is tested for specific values of the operating conditions; however, it has general validity and can be used in place of typical procedures based on empirical models or engineers’ experience for the industrial design of tangential inlet cyclone separators with low solid loading.
Originality/value
Such an optimization process has never been proposed before for the design of cyclone separators; it has been developed with the aim of being both highly accurate and compatible with the industrial design time.
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João Guilherme Alves Correa, Josivan Leite Alves, Aline Sacchi Homrich and Marly Monteiro de Carvalho
Investigate distinctive skills, encompassing Building Information Modeling (BIM skills, project management (PM) skills (PMSs), as well as strategic and operational skills (OSs…
Abstract
Purpose
Investigate distinctive skills, encompassing Building Information Modeling (BIM skills, project management (PM) skills (PMSs), as well as strategic and operational skills (OSs) in the architecture, engineering and construction (AEC) industry.
Design/methodology/approach
The research design adopts a quantitative survey-based approach, utilizing a partial least squares structural equation modeling (PLS-SEM).
Findings
The findings underscore a significant relationship between OSs and both BIM and PMSs, while also illuminating the relationship of strategic skills with both BIM and PMSs. However, intriguingly, the study reveals that although BIM skills and PMSs are indispensable, they lack a statistically significant relationship. Despite this, we have identified a pathway from BIM skills to operational and strategic skills that traverses through PMSs, exhibiting significant indirect effects.
Research limitations/implications
Our study employs cross-sectional data rather than longitudinal data, which hinders temporal interpretations of the associations between competence building for AEC professionals especially given that BIM skills are still in the early stages within AEC projects, particularly in Latin America. Therefore, a longitudinal study would offer deeper insights into potential causation, allowing for a more robust establishment of underlying associations. Additionally, future research endeavors should focus on capturing longitudinal data through case studies that explore perceptions and observations of the roles of BIM managers and project managers in project-based organizations.
Practical implications
Our model guides organizations to recognize the importance of BIM management skills as a pivotal role in the AEC industry, bridging operational and strategic levels. While project managers focus on tools that facilitate team and project integration, BIM managers enhance collaboration and communication across different disciplines within construction projects. This synthesis highlights the complementary roles of project managers and BIM managers in driving successful project outcomes, showcasing the synergy between their skill sets in achieving strategic objectives within the AEC industry. Furthermore, it underscores the critical role of indirect and cascading flows of influence among skill domains through multiple interconnected pathways.
Originality/value
Our study demonstrates that different types of skills are required to manage AEC projects. These skills are interconnected through direct and indirect pathways that warrant attention from academia. The theoretical contribution of the research model is clearly shown in the linking between PM and BIM in the AEC industry. Several scholars recognize BIM as innovative and that drives the success of civil construction projects; however, our study goes further in identifying the significant relationships among variables and the magnitude of their effects on strategic and OSs for BIM management and PM domain.
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Mohamed S. Mahfouz, Abdulwahab Aqeeli, Anwar M. Makeen, Ramzi M. Hakami, Hatim H. Najmi, Abdullkarim T. Mobarki, Mohammad H. Haroobi, Saeed M. Almalki, Mohammad A. Mahnashi and Osayd A. Ageel
The issue of mental health literacy has been widely studied in developed countries, with few studies conducted in Arab countries. In this study we aimed to investigate mental…
Abstract
The issue of mental health literacy has been widely studied in developed countries, with few studies conducted in Arab countries. In this study we aimed to investigate mental health literacy and attitudes towards psychiatric patients among students of Jazan University, Kingdom of Saudi Arabia. A cross-sectional study was conducted among undergraduate students using a validated Arabic-version questionnaire. A total of 557 students were recruited from different Jazan university colleges. The majority of students (90.3%) have intermediate mental health literacy. Regarding the etiology of mental illness, students agreed that genetic inheritance (45.8%), poor quality of life (65%) and social relationship weakness (73.1%) are the main causes of mental illness. The majority thought that mentally ill people are not capable of true friendships (52.5%) and that anyone can suffer from a mental illness (49.4%). Students' attitudes towards psychiatric patients were mixed, with 68.7% reporting that they could maintain a friendship with a mentally ill person and that people with mental illness should have the same rights as anyone else (82.5%). Mental health literacy among university students was intermediate. There is an urgent need for health educational programs to change the attitudes of students regarding this important health issue.
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Najib Mahfuz is the first Arab‐language author to win the Nobel Prize in literature. Born in 1911 the son of a middle‐class Jamaliyah merchant, he became the most popular novelist…
Abstract
Najib Mahfuz is the first Arab‐language author to win the Nobel Prize in literature. Born in 1911 the son of a middle‐class Jamaliyah merchant, he became the most popular novelist in Egypt and the Arab countries.