Chatzikonstantinou, ioannis
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Name Variants
Ioannis Chatzikonstantinou
Job Title
Öğrt.Gör.
Email Address
Main Affiliation
01.01.10.02. Mimarlık Bölümü
Status
Former Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Sustainable Development Goals
1NO POVERTY
0
Research Products
2ZERO HUNGER
2
Research Products
3GOOD HEALTH AND WELL-BEING
2
Research Products
4QUALITY EDUCATION
0
Research Products
5GENDER EQUALITY
0
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6CLEAN WATER AND SANITATION
0
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7AFFORDABLE AND CLEAN ENERGY
2
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8DECENT WORK AND ECONOMIC GROWTH
0
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9INDUSTRY, INNOVATION AND INFRASTRUCTURE
2
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10REDUCED INEQUALITIES
0
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11SUSTAINABLE CITIES AND COMMUNITIES
4
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12RESPONSIBLE CONSUMPTION AND PRODUCTION
2
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13CLIMATE ACTION
0
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14LIFE BELOW WATER
3
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15LIFE ON LAND
0
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16PEACE, JUSTICE AND STRONG INSTITUTIONS
0
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17PARTNERSHIPS FOR THE GOALS
1
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This researcher does not have a Scopus ID.
Documents
22
Citations
421
Scholarly Output
36
Articles
7
Views / Downloads
0/0
Supervised MSc Theses
0
Supervised PhD Theses
0
WoS Citation Count
397
Scopus Citation Count
514
Patents
0
Projects
0
WoS Citations per Publication
11.03
Scopus Citations per Publication
14.28
Open Access Source
2
Supervised Theses
0
| Journal | Count |
|---|---|
| IEEE Congress on Evolutionary Computation (CEC) | 6 |
| IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI) | 5 |
| IEEE Congress on Evolutionary Computation CEC 2015 | 5 |
| 2016 IEEE Congress on Evolutionary Computation CEC 2016 | 5 |
| Architectural Science Review | 2 |
Current Page: 1 / 3
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36 results
Scholarly Output Search Results
Now showing 1 - 10 of 36
Article Citation - Scopus: 21Addressing design preferences via auto-associative connectionist models: Application in sustainable architectural Façade design(Elsevier B.V., 2017) Ioannis Chatzikonstantinou; I. Sevil Sariyildiz; Chatzikonstantinou, Ioannis; Sariyildiz, I. SevilTruly successful designs are characterized by both satisfaction of design goals and the presence of desirable physical features. Experienced design professionals are able to exercise their cognition to satisfy both aspects to a high degree. However complex design tasks represent challenges for human cognition and as such computational decision support systems emerge as a relevant topic. We present a computational decision support framework for treating preferences related to physical design features. The proposed framework is based on auto-associative machine learning models that inductively learn relationships between design features characterizing highly performing designs. The knowledge matter to be learned is derived through multi-objective stochastic optimization. The resulting auto-associative models are excited with a preference vector containing a favorable composition of design features. The models are able to alleviate those relationships that result in shortcomings of performance. The model thus outputs well performing design solution where preferences pertaining to physical features are also satisfied to the extent possible. The paper focuses on the applicability of the proposed approach in architectural design as an exceptional example of complex design discusses methods to evaluate model performance and validates the proposed method through an application focusing on the design of a sustainable façade. © 2017 Elsevier B.V. All rights reserved.Conference Object Multi-objective optimization for shading devices in buildings by using evolutionary algorithms(Institute of Electrical and Electronics Engineers Inc., 2016) Ayca Kirimtat; Basak Kundakci Koyunbaba; Ioannis Chatzikonstantinou; I. Sevil Sariyildiz; Ponnuthurai Nagaratnam SuganthanThe reduction of energy consumption is a major challenge around the world. Architectural aspects have a significant place to minimize energy consumption to the maximum level. The use of large glazed facades causes overheating problems in certain climatic regions. Shading elements must be considered at an early stage in the design process to overcome this problem. An application of the method is presented focusing on the horizontal louvers integrated to a building in Izmir Turkey. The contributions of the paper can be summarized as follows. We show that most architectural design problems are basically real-parameter multi-objective constrained optimization problems. So any type of evolutionary and swarm optimization methods can be used in this field. A multi-objective self-adaptive differential evolution algorithm (jDEMO) inspired from the DEMO algorithm from the literature with some modifications is developed and compared to the well-known fast and non-dominated sorting genetic algorithm so called NSGA-II in order to solve this complex problem and identify alternative design solutions to decision makers. Through the experimental results we show that the proposed algorithm generated slightly better results when comparing to the NSGA-II algorithm. © 2017 Elsevier B.V. All rights reserved.Conference Object Conceptual airport terminal design using evolutionary computation(Institute of Electrical and Electronics Engineers Inc., 2015) Ioannis Chatzikonstantinou; I. Sevil Sariyildiz; Michael S. BittermannPassenger terminals are very complex buildings not only in their function form and structure but also in infrastructure security comfort energy which deal with huge investments both in terms of capital as well as in terms of resources and environmental impact. As such it is expected that they are designed to fulfill their purpose while minimizing their negative aspects to the environment. Identifying design solutions that satisfy these goals is a challenging task due to the complexity involved. The design task is characterized by excessive number of solutions conflicting goals and complex relations between design decision variables objectives and constraints. As such appropriate informed decisions that integrate as many design aspects as possible should be ensured as early as the conceptual stage of the design. In this study the problem of conceptual airport terminal design is addressed by means of computational decision support methodologies. The proposed method is based on the integration of the following components: i. A parametric modeling approach for enabling the instantaneous generation of a wide variety of designs II. A multi-faceted evaluation scheme which integrates functional energy and architectural aspects IIi. A Multi-Objective Genetic Algorithm namely the NSGA-II to identify well performing solutions. A computational model implementing the method is outlined and validation of the method is performed based on two different scenarios corresponding to commonly occurring airport configurations. The performance of two optimization runs with different population sizes as well as qualitative aspects of the resulting solutions is discussed. © 2017 Elsevier B.V. All rights reserved.Conference Object Citation - WoS: 7Citation - Scopus: 9Multi-Objective Optimization Through Differential Evolution for Restaurant Design(IEEE, 2016) Cemre Cubukcuoglu; Ioannis Chatzikonstantinou; Berk Ekici; Sevil Sariyildiz; M. Fatih Tasgetiren; Ekici, Berk; Sariyildiz, Sevil; Chatzikonstantinou, Ioannis; Tasgetiren, M. Fatih; Cubukcuoglu, CemreThis paper presents the results obtained by NSGA-II and jDEMO on a restaurant design optimization in the conceptual phase. A multi-objective problem is formulated by considering the minimization of investment and the maximization of customer count and maximization of visual perception subject to several constraints. The main problem requires the configuration of restaurant spaces with different seating groups decisions regarding the customer capacity fraction and position of the windows. The contributions of the paper can be summarized as follows. We show that most architectural design problems are basically real-parameter multi-objective constrained optimization problems. So any type of evolutionary and swarm optimization methods can be used in this field. A multi-objective self-adaptive differential evolution algorithm (jDEMO) inspired from the DEMO algorithm from the literature with some modifications is developed and compared to the well-known fast and non-dominated sorting genetic algorithm so called NSGA-II in order to solve this complex problem and identify alternative design solutions to decision makers. Through the experimental results we show that the proposed algorithm is competitive with the NSGA-II algorithm.Conference Object Citation - WoS: 9Citation - Scopus: 13Designing Self-Sufficient Floating Neighborhoods Using Computational Decision Support(IEEE, 2015) Ayca Kirimtat; Ioannis Chatzikonstantinou; Sevil Sariyildiz; Ayca Tartar; Sariyildiz, Sevil; Tartar, Ayca; Chatzikonstantinou, Ioannis; Kirimtat, AycaFloating settlements which introduce further design complexities over traditional developments have become an alternative for urban development due to climate change and shortage of land. This study aims to develop a floating settlement concept that presents an approach to the design of floating neighborhoods using parametric modelling techniques in combination with Intelligent Decision Support tools and optimization methods. Optimization results of two algorithms namely NSGA-II and DE are compared regarding to objectives. The objectives considered in this study are walkability within the neighborhoods scenic view and cost-effectiveness. Results suggest that DE performs better than NSGA-II in this problem. An application of the method is presented focusing on the design of floating neighborhoods in the project area namely Urla which is a seaside place along the coastline of Izmir Turkey.Conference Object Engineering Performance Simulations in Architectural Design Conception Atrium in Shenyang: a case study on thermal mass(Education and research in Computer Aided Architectural Design in Europe, 2013) Michela Turrin; Ioannis Chatzikonstantinou; Martin J. Tenpierik; I. Sevil Sariyildiz; Turrin, Michela; Sariyildiz, Sevil; Chatzikonstantinou, Ioannis; Tenpierik, Martin; R. Stouffs , S. SariyildizThe paper tackles the integration of engineering performance simulations in the conceptual phase of architectural design with specific focus on parametric design processes. A general framework is exemplified in which the use of performance simulations and the learning process of the designer are discussed in relation to the parameterization process. A specific case study is presented more in details regarding the design of an atrium for the reuse of an existing building in Shenyang-China. Performance simulations concerning the thermal comfort in the atrium are presented and discussed in relation to the general framework. © 2022 Elsevier B.V. All rights reserved.Conference Object Citation - WoS: 9Citation - Scopus: 10Multi-Objective skylight optimization for a healthcare facility foyer space(Institute of Electrical and Electronics Engineers Inc., 2017) Muhittin Yufka; Berk Ekici; Cemre Cubukcuoglu; Ioannis Chatzikonstantinou; I. Sevil Sariyildiz; Ekici, Berk; Chatzikonstantinou, Ioannis; Sariyildiz, I. Sevil; Yufka, Muhittin; Cubukcuoglu, CemreIn this paper the design of a specific case study of a foyer space is concerned in healthcare facility. The design task of a healthcare facility in architectural perspective is one of the most challenging tasks in the architectural design field since it involves different spaces that have unique requirements. Specifically a foyer space has been considered as a gathering area that answers people's needs and expectations. The study shows an application of computational intelligence for a skylight design in foyer space. For this reason objective functions are considered to minimize skylight cost and to maximize the daylight performance of the interior space. Multi-Objective Self-Adaptive Ensemble Differential Evolution Algorithm and Non-Dominated Sorting Genetic Algorithm-II are proposed to tackle this complex problem. According to results jE-DEMO algorithm presents satisfactory solutions as well as NSGA-II. © 2017 Elsevier B.V. All rights reserved.Conference Object Multi-Objective Skylight Optimization for a Healthcare Facility Foyer Space(IEEE, 2017) Muhittin Yufka; Berk Ekici; Cemre Cubukcuoglu; Ioannis Chatzikonstantinou; I. Sevil SariyildizIn this paper the design of a specific case study of a foyer space is concerned in healthcare facility. The design task of a healthcare facility in architectural perspective is one of the most challenging tasks in the architectural design field since it involves different spaces that have unique requirements. Specifically a foyer space has been considered as a gathering area that answers people's needs and expectations. The study shows an application of computational intelligence for a skylight design in foyer space. For this reason objective functions are considered to minimize skylight cost and to maximize the daylight performance of the interior space. Multi-Objective Self-Adaptive Ensemble Differential Evolution Algorithm and Non-Dominated Sorting Genetic Algorithm-II are proposed to tackle this complex problem. According to results jE_DEMO algorithm presents satisfactory solutions as well as NSGA-II.Conference Object Citation - WoS: 11Citation - Scopus: 12A multi-objective self-adaptive differential evolution algorithm for conceptual high-rise building design(Institute of Electrical and Electronics Engineers Inc., 2016) Berk Ekici; Ioannis Chatzikonstantinou; I. Sevil Sariyildiz; M. Fatih Tasgetiren; Quanke Pan; Ekici, Berk; Sariyildiz, Sevil; Chatzikonstantinou, Ioannis; Tasgetiren, M. Fatih; Pan, Quan-KeThis paper presents a multi-objective self-adaptive differential evolution algorithm to solve the form-finding problem of high-rise building design in the conceptual phase. The aim of the research is to reach suitable high-rise design alternatives for hard and soft objectives which are construction cost per square meter structural displacement and visual perception of the spaces from the inside out subject to several constraints that are related with both high-rise construction regulations and profitability of the spaces. We formulate the problem as a multi-objective realparameter constrained optimization problem for three objectives that are inherently conflicting. To tackle this problem we developed two different optimization algorithms namely a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and a Self-Adaptive Differential Evolution Algorithm (jDE) in order to obtain Pareto fronts with diversified non-dominated solutions. The extensive computational results show that the jDE algorithm yields much more desirable Pareto front than the NSGA-II algorithm. © 2017 Elsevier B.V. All rights reserved.Conference Object pCOLAD: online sharing of parameters for collaborative architectural design(ECAADE-EDUCATION & RESEARCH COMPUTER AIDED ARCHITECTURAL DESIGN EUROPE, 2014) Hans J. C. Hubers; Michela Turrin; Irem Erbas; Ioannis Chatzikonstantinou; EM ThompsonSimultaneous interdisciplinary architectural design from the very start of a project faces challenges in properly sharing information across disciplines. This research developed a method and related digital tool to improve collaborative design and aimed at making selected information to be shared faster and more transparently. The method consists of developing alternative parametric solutions for different parts of the design in such a way that crucial parameters form a link between these parts. The digital tool has been developed for Grasshopper and permits synchronic (real-time over the Internet) and a-synchronic sharing of these parameters. The design alternatives are evaluated with specific criteria pros and cons in an Internet Forum and discussed via a video-conferencing tool. Decisions are then taken in a collaborative manner through voting. The paper describes the method based on a case study.
