Suchergebnisse - "Toğan, Vedat"

Evaluating Performance Appraisal Effects on Employee Motivation and Productivity: Insights from the Turkish Construction Industry via Covariance-Based Structural Equation Modeling.

Autoren: Temel, Bayram Ali Semercioğlu, İpek Naz Başağa, Hasan Basri et al.

Quelle: Buildings (2075-5309); Nov2025, Vol. 15 Issue 22, p4040, 17p

Schlagwörter: EMPLOYEE motivation, LABOR productivity, EVALUATION methodology, EMPLOYEE reviews, STRUCTURAL equation modeling, CONSTRUCTION industry, JUDGMENT (Psychology)

Geografische Kategorien: TURKEY

Construction Delay Prediction Model Using a Relationship-Aware Multihead Graph Attention Network.

Autoren: Mostofi, Fatemeh Tokdemir, Onur Behzat Toğan, Vedat

Quelle: Journal of Management in Engineering; May2025, Vol. 41 Issue 3, p1-16, 16p

Schlagwörter: CONSTRUCTION delays, CONSTRUCTION management, PREDICTION models, PRODUCTION scheduling, DECISION making

Performance‐driven contractor recommendation system using a weighted activity–contractor network.

Autoren: Mostofi, Fatemeh Tokdemir, Onur Behzat Bahadır, Ümit et al.

Quelle: Computer-Aided Civil & Infrastructure Engineering; 1/20/2025, Vol. 40 Issue 3, p409-424, 16p

Schlagwörter: KEY performance indicators (Management), MACHINE learning, CONSTRUCTION contractors, DECISION making, BUILDING design & construction, COMMUNICATION network analysis, SUPERVISED learning

A dual opposition learning-based multi-objective Aquila Optimizer for trading-off time-cost-quality-CO2 emissions of generalized construction projects.

Autoren: Eirgash, Mohammad Azim Toğan, Vedat

Quelle: Engineering Computations; 2024, Vol. 41 Issue 8/9, p2074-2101, 28p

Schlagwörter: SOCIAL groups, CONSTRUCTION projects, BLENDED learning, MACHINE learning, ALGORITHMS

Geografische Kategorien: L'AQUILA (Italy)

Predicting Construction Accident Outcomes Using Graph Convolutional and Dual-Edge Safety Networks.

Autoren: Mostofi, Fatemeh Toğan, Vedat

Quelle: Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ); Oct2024, Vol. 49 Issue 10, p13315-13332, 18p

Schlagwörter: CONSTRUCTION management, PREDICTION models, INFORMATION networks, TEST design, FORECASTING, WORK-related injuries

Predicting the Cost of Rework in High-Rise Buildings Using Graph Convolutional Networks.

Autoren: Mostofi, Fatemeh Tokdemir, Onur Behzat Toğan, Vedat et al.

Quelle: Journal of Construction Engineering & Management; Aug2024, Vol. 150 Issue 8, p1-17, 17p

Schlagwörter: SKYSCRAPERS, CONSTRUCTION project management, TALL buildings, CONSTRUCTION management, PREDICTION models, CONSTRUCTION projects

Automatic landslide detection and visualization by using deep ensemble learning method.

Autoren: Hacıefendioğlu, Kemal Varol, Nehir Toğan, Vedat et al.

Quelle: Neural Computing & Applications; Jun2024, Vol. 36 Issue 18, p10761-10776, 16p

Schlagwörter: LANDSLIDES, CONVOLUTIONAL neural networks, DEEP learning, REMOTE-sensing images, NATURAL disasters, REMOTE sensing

Structural Optimization of Trusses in Building Information Modeling (BIM) Projects Using Visual Programming, Evolutionary Algorithms, and Life Cycle Assessment (LCA) Tools.

Autoren: Yavan, Feyzullah Maalek, Reza Toğan, Vedat

Quelle: Buildings (2075-5309); Jun2024, Vol. 14 Issue 6, p1532, 21p

Schlagwörter: STRUCTURAL optimization, PRODUCT life cycle assessment, BUILDING information modeling, ARTIFICIAL intelligence, GLOBAL optimization

A Data-Driven Recommendation System for Construction Safety Risk Assessment.

Autoren: Mostofi, Fatemeh Toğan, Vedat

Quelle: Journal of Construction Engineering & Management; Dec2023, Vol. 149 Issue 12, p1-14, 14p

Schlagwörter: RECOMMENDER systems, SYSTEM safety, RISK assessment, REPRESENTATIONS of graphs

Multiedge Graph Convolutional Network for House Price Prediction.

Autoren: Mostofi, Fatemeh Toğan, Vedat Başağa, Hasan Basri et al.

Quelle: Journal of Construction Engineering & Management; Nov2023, Vol. 149 Issue 11, p1-15, 15p

Schlagwörter: HOME prices, MACHINE learning, HOUSING market, RESIDENTIAL patterns, CONSTRUCTION management, BUILDING design & construction

Doğu Karadeniz Bölgesinde planlanan demiryolu güzergâhlarının AHP ve TOPSIS yöntemleriyle sıralanması ve seçimi. (Turkish)

Alternate Title: Prioritising and selecting the railway routes planned in the Eastern Black Sea Region with the AHP and TOPSIS methods. (English)

Autoren: BOZ, Ediz TÜRK, Ersin TOĞAN, Vedat

Quelle: Pamukkale University Journal of Engineering Sciences; 2023, Vol. 29 Issue 6, p617-627, 11p

Schlagwörter: ANALYTIC hierarchy process, MULTIPLE criteria decision making, RAILROAD design & construction, RAILROADS, CONSTRUCTION delays, SILK Road

Comprehensive Root Cause Analysis of Construction Defects Using Semisupervised Graph Representation Learning.

Autoren: Mostofi, Fatemeh Tokdemir, Onur Behzat Toğan, Vedat

Quelle: Journal of Construction Engineering & Management; Sep2023, Vol. 149 Issue 9, p1-13, 13p

Schlagwörter: REPRESENTATIONS of graphs, ROOT cause analysis, ASSOCIATION rule mining, COST overruns, CONSTRUCTION management, SEMANTICS, DATA collection platforms

Construction Safety Risk Model with Construction Accident Network: A Graph Convolutional Network Approach.

Autoren: Mostofi, Fatemeh Toğan, Vedat Ayözen, Yunus Emre et al.

Quelle: Sustainability (2071-1050); Dec2022, Vol. 14 Issue 23, p15906, 18p

Predicting the Impact of Construction Rework Cost Using an Ensemble Classifier.

Autoren: Mostofi, Fatemeh Toğan, Vedat Ayözen, Yunus Emre et al.

Quelle: Sustainability (2071-1050); Nov2022, Vol. 14 Issue 22, p14800, 19p

Damage detection in anisotropic-laminated composite beams based on incomplete modal data and teaching–learning-based optimization.

Autoren: Şimşek, Sebahat Kahya, Volkan Adıyaman, Gökhan et al.

Quelle: Structural & Multidisciplinary Optimization; Nov2022, Vol. 65 Issue 11, p1-15, 15p

CAM-K: a novel framework for automated estimating pixel area using K-Means algorithm integrated with deep learning based-CAM visualization techniques.

Autoren: Hacıefendioğlu, Kemal Mostofi, Fatemeh Toğan, Vedat et al.

Quelle: Neural Computing & Applications; Oct2022, Vol. 34 Issue 20, p17741-17759, 19p

Schlagwörter: DEEP learning, K-means clustering, CONVOLUTIONAL neural networks, PIXELS

Wood construction damage detection and localization using deep convolutional neural network with transfer learning.

Autoren: Hacıefendioğlu, Kemal Ayas, Selen Başağa, Hasan Basri et al.

Quelle: European Journal of Wood & Wood Products / Holz als Roh- und Werkstoff; Aug2022, Vol. 80 Issue 4, p791-804, 14p

Vibration-based damage detection in anisotropic laminated composite beams by a shear-deformable finite element and harmony search optimization.

Autoren: Kahya, Volkan Şimşek, Sebahat Toğan, Vedat

Quelle: Structural & Multidisciplinary Optimization; Jun2022, Vol. 65 Issue 6, p1-19, 19p

Optimizing of Discrete Time-Cost in Construction Projects Using New Adaptive Weight Formulations.

Autoren: Toğan, Vedat Berberoğlu, Neslihan Dede, Tayfun

Quelle: KSCE Journal of Civil Engineering; Feb2022, Vol. 26 Issue 2, p511-521, 11p

Evaluation of structural lightweight concrete in terms of energy performance: A case study.

Alternate Title: Taşıyıcı hafif betonun enerji performansı açısından değerlendirilmesi: Bir örnek çalışma. (Turkish)

Autoren: NAYIR, Safa BAHADIR, Ümit ERDOĞDU, Şakir et al.

Quelle: Pamukkale University Journal of Engineering Sciences; 2021, Vol. 27 Issue 6, p696-702, 7p

Schlagwörter: REINFORCED concrete, LIGHTWEIGHT concrete, THERMAL conductivity, FLY ash, PERFORMANCE theory, COOLING loads (Mechanical engineering)