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İNŞAAT SEKTÖRÜNDE AKILLI SENSÖR TEKNOLOJİLERİ İLE ÇALIŞAN PERFORMANSININ ÖLÇÜMÜ VE DEĞERLENDİRİLMESİ

Yıl 2025, Cilt: 11 Sayı: 2, 45 - 62, 24.12.2025

Öz

İnşaat sektörü insanlığın barınma ihtiyacını karşılama amacı ile medeniyetlerin kurulmaya başladığı zamandan günümüze aktif bir sektördür. Bu sektörün geçmişte ve günümüzdeki en önemli sorunlarından biri çalışan performanslarının yönetilmesidir. İnşaat sektörü teknoloji ve akıllı cihaz kullanımları ile sürekli gelişmekte ve dönüşüm geçirmektedir. Bu dönüşümün önemli bir bileşeni, inşaat projelerinde kullanılan akıllı sensörlerdir. Bu çalışma, inşaat sektöründe Raspberry Pi Pico, MPU-6050 Gyro Sensor ve RTC DS1302 zaman modülü gibi akıllı cihazlar kullanılarak çalışan performansının değerlendirilmesini ele almaktadır. Çalışmanın temel odak noktası, inşaat çalışanlarının hareketlerini, fiziksel durumlarını ve çalışma koşullarını izlemek ve analiz etmek için sensörlerin kullanımıdır. MPU-6050, hareket ve eğilme verilerini hassas bir şekilde ölçerken, RTC DS1302 zaman modülü verilerin zaman damgasını oluşturur. Ölçümlenen veriler, Raspberry Pi Pico üzerinden toplanır ve PHP ile MySQL veri tabanına kaydedilir. Veri tabanında saklanan veriler, çalışanların günlük performanslarını analiz etmek için kullanılır. Bu analiz, çalışan verimliliği, güvenlik riskleri ve çalışma koşullarının iyileştirilmesi gibi faktörleri içerir. Sonuçlar, inşaat sektörünün daha verimli, güvenli ve sürdürülebilir bir şekilde yönetilmesine yardımcı olabilir.

Etik Beyan

Bu çalışma, birinci yazarın ikinci yazar danışmanlığında tamamladığı “İnşaat Projelerinde Akıllı Sensörler Kullanılarak Çalışan Performanslarının Değerlendirilmesi” başlıklı yüksek lisans tezinden üretilmiştir.

Kaynakça

  • Ahmed, M., Xu, W. (2023) Investigation of Pile Construction and Productivity Loss: An Analysis of Macro Impact Factor. World Journal of Engineering and Technology, 11, 932-964. doi: 10.4236/wjet.2023.114062.
  • Anderson K,McAdam R (2004). A critique of benchmarking and performance measurement: Lead or lag?. Benchmarking: An International Journal, Vol. 11 No. 5 pp. 465–483, doi: https://doi.org/10.1108/14635770410557708
  • Bassioni, H., Price, A. and Hassan, T. (2004). Theoretical formulation of a framework for measuring business performance in construction. IN: Proceedings of 2004 International Built and Human Environment Research Week, Salford, Great Britain, 29 March-2 April 2004, pp.419-430.
  • Çambel, E. ve Özgan, E. (2018). Mimari tasarım sürecinde mühendislik sorunlarının mimarlar açısından incelenmesi. Journal of Advanced Technology Sciences. 7(2): 47-70.
  • Çıdık, M. S. (2008). Türk inşaat sektöründeki bilgi yönetimi uygulamalarında yaşanan problemler ve çözüm önerileri. Yayımlanmamış Yüksek Lisans Tezi. İstanbul Teknik Üniversitesi – Fen Bilimleri Enstitüsü – Mimarlık Anabilim Dalı.
  • Elalwani, E. & Çalışkan, E. B. (2024). Integrating BIM technology in construction for effective knowledge management: case studies and methodological insights. Turkish Journal of Engineering, 8 (4), 647-655. Sun, J., Xu, K., ve Chen, P. (2022). IoT-enabled cyber–physical systems for construction worker tracking and safety monitoring. Automation in Construction, 137: 104218.
  • Eşkinat, R. ve Tepecik, F. (2012). İnşaat sektörüne küresel bakış. Afyon Kocatepe Üniversitesi İİBF Dergisi, 14(1): 25-41.
  • Fasoyinu, A. A., Azhar, S., Sattineni, A., Toyin, J. O. (2025). Wearable sensing devices for construction safety: Research trends, applications, challenges, and future opportunities. Automation in Construction, 179, 106424. https://doi.org/10.1016/j.autcon.2025.106424
  • Grünberg, T. (2004). Performance improvement: towards a method for finding and prioritising potential performance improvement areas in manufacturing operations. Emerald; Emerald Group Publıshıng Limited, 52-71, doi:https://doi.org/10.1108/17410400410509969
  • He, Y., He, J., Wen, N. (2023). The challenges of IoT based applications in high risk environments, health and safety industries in the Industry 4.0 era using decision making approach. Journal of Innovation & Knowledge, 8(2), 100347. https://doi.org/10.1016/j.jik.2023.100347 International Organization for Standardization. (2018). ISO 19650-1: Organization and digitization of information about buildings and civil engineering works, including building information modelling (BIM) — Information management using building information modelling — Part 1: Concepts and principles. ISO.
  • Kanan, R., Elhassan, O., Bensalem, R. (2018). An IoT-based autonomous system for workers' safety in construction sites with real-time alarming, monitoring, and positioning strategies. Automation in Construction, volume 88, pages 73-86. https://doi.org/10.1016/j.autcon.2017.12.033
  • Kim, J. H., Jo, B. W., Jo, J. H., & Kim, D. K. (2020). Development of an IoT-based construction worker physiological data monitoring platform at high temperatures. Sensors, 20(19), 5682. https://doi.org/10.3390/s20195682
  • Kim, J., Lee, K., ve Jeon, J. (2024). Systematic literature review of wearable devices and data analytics for construction safety and health. Expert Systems with Applications, 257: 125038. https://doi.org/10.1016/j.eswa.2024.125038
  • Laudon, K. C., ve Laudon, J. P. (2020). Management information systems: Managing the digital firm. Pearson.
  • Li, J., Chen, G., Antwi Afari, M. F. (2024). Recognizing sitting activities of excavator operators using multi sensor data fusion with machine learning and deep learning algorithms. Automation in Construction, 165, Article 105554. https://doi.org/10.1016/j.autcon.2024.105554
  • Ma, J., Li, H., Wang, L., Yu, X., Huang, X. (2025). Multimodal fusion for monitoring worker fatigue in elevated work environments. Advanced Engineering Informatics, 67, 103565. https://doi.org/10.1016/j.aei.2025.103565
  • McKinsey Global Institute. (2017). Reinventing construction: A route to higher productivity. McKinsey & Company.
  • Medori D, Steeple D (2000). A framework for auditing and enhancing performance measurement systems. International Journal of Operations & Production Management, Vol. 20 No. 5 pp. 520–533, doi: https://doi.org/10.1108/01443570010318896
  • Mekruksavanich, S., & Jitpattanakul, A. (2025). Construction worker activity recognition using deep residual convolutional network based on fused IMU sensor data in internet-of-things environment. IoT, 6(3), 36. https://doi.org/10.3390/iot6030036
  • Naranjo, J. E., Mora, C. A., Bustamante Villagómez, D. F., Mancheno Falconi, M. G., Garcia, M. V. (2025). Wearable sensors in ındustrial ergonomics: Enhancing safety and productivity in industry 4.0. Sensors, 25(5), 1526. https://doi.org/10.3390/s25051526
  • Neely, A., Gregory, M., Platts, K., (1995), Performance measurement system design: A literature review and research agenda. International Journal of Operations & Production Management, Vol. 15 Iss: 4, pp. 80 - 116.
  • Sabino, I., Fernandes, M. d. C., Cepeda, C., Quaresma, C., Gamboa, H., Nunes, I. L., Gabriel, A. T. (2024). Application of wearable technology for the ergonomic risk assessment of healthcare professionals: A systematic literature review. International Journal of Industrial Ergonomics, 100, Article 103570. https://doi.org/10.1016/j.ergon.2024.103570
  • Shvets, Y., & Hanák, T. (2023). Use of the Internet of Things in the construction industry and facility management: Usage examples overview. Procedia Computer Science, 219, 1670–1677. https://doi.org/10.1016/j.procs.2023.01.460
  • Shakerian, S., Habibnezhad, M., Ojha, A., Lee, G., Liu, Y., Jebelli, H., Lee, S. (2021). Assessing occupational risk of heat stress at construction: A worker‑centric wearable sensor‑based approach. Safety Science, 142, 105395. https://doi.org/10.1016/j.ssci.2021.105395
  • Slack, N. (2010). Operations management. New York: Financial Times Prentice Hall. ISBN: 9780273731603.
  • Śliwa, D. (2025, March 12). Internet of Things (IoT) in construction: Safety and optimization of construction processes. Univio. https://www.univio.com/blog/internet-of-things-iot-in-construction-safety-and-optimization-of-construction-processes/
  • Tangen, S. (2005). Demystifying productivity and performance. International Journal of Productivity and Performance Management, Vol. 54 No. 1 pp. 34–46, doi: https://doi.org/10.1108/17410400510571437
  • Tecim, V. (2023). YBS Piramidi. https://vahaptecim.com.tr/yonetim-bilisim-sistemleri/, (30.12.2023).
  • Tezcan, Ö., Akcay, C., Sari, M., Cavus, M. (2025). Sensor-Based Automatic Recognition of Construction Worker Activities Using Deep Learning Network. Sensors, 25(13), 3988. https://doi.org/10.3390/s25133988
  • Uğur, L. O. (2006). İnşaat sektöründe riskler ve risk yönetimi – seminer notları. Türkiye Müteahhitler Birliği. http://www.insulaelibertatis.com/ KitaKita/Insaat_Sektorunde_Risk_yonetimi.pdf, (30.12.2023).
  • Uğural, M. N. (2020). İnşaat projelerinde zaman maliyet ödünleşim problemi: Örnek olay analizi. Avrupa Bilim ve Teknoloji Dergisi (19), 460-465. https://doi.org/10.31590/ejosat.726891
  • Wang, J. , Zhu, G. , Wu, S. and Luo, C. (2021) Worker’s helmet recognition and identity recognition based on deep learning. Open Journal of Modelling and Simulation, 9, 135-145. doi: 10.4236/ojmsi.2021.92009.
  • Wang, M., Chen, J., & Ma, J. (2024). Monitoring and evaluating the status and behaviour of construction workers using wearable sensing technologies. Automation in Construction, 165, 105555. https://doi.org/10.1016/j.autcon.2024.105555
  • World Economic Forum & Boston Consulting Group. (2016). Shaping the future of construction: A breakthrough in mindset and technology. World Economic Forum.
  • Yurtcu, A. (2025). Wearable technologies and internet of things for real-time hazard monitoring in occupational health and safety: A systematic content analysis. Innovative Approaches to Engineering Problems, 1(2), 45-54.

MEASUREMENT AND EVALUATION OF EMPLOYEE PERFORMANCE USING SMART SENSOR TECHNOLOGIES IN THE CONSTRUCTION SECTOR

Yıl 2025, Cilt: 11 Sayı: 2, 45 - 62, 24.12.2025

Öz

The construction industry has been a vital sector since the dawn of civilization, driven by humanity's fundamental need for shelter. One of the most significant challenges in this sector, both past and present, is managing employee performance. The construction industry is constantly evolving and transforming through the use of technology and smart devices. A crucial component of this transformation is the use of smart sensors in construction projects. This study evaluates employee performance in the construction sector using smart devices, including the Raspberry Pi Pico, MPU-6050 Gyro Sensor, and RTC DS1302 time module. The primary focus of the study is the use of sensors to monitor and analyze the movements, physical condition, and working conditions of construction workers. The MPU-6050 precisely measures movement and bending data, while the RTC DS1302 time module creates a timestamp of the data. The measured data is collected via the Raspberry Pi Pico and saved to a MySQL database using PHP. The data stored in the database is used to analyze the daily performance of employees. This analysis encompasses factors such as employee productivity, safety risks, and improvements to working conditions. The results can help the construction industry operate more efficiently, safely, and sustainably.

Kaynakça

  • Ahmed, M., Xu, W. (2023) Investigation of Pile Construction and Productivity Loss: An Analysis of Macro Impact Factor. World Journal of Engineering and Technology, 11, 932-964. doi: 10.4236/wjet.2023.114062.
  • Anderson K,McAdam R (2004). A critique of benchmarking and performance measurement: Lead or lag?. Benchmarking: An International Journal, Vol. 11 No. 5 pp. 465–483, doi: https://doi.org/10.1108/14635770410557708
  • Bassioni, H., Price, A. and Hassan, T. (2004). Theoretical formulation of a framework for measuring business performance in construction. IN: Proceedings of 2004 International Built and Human Environment Research Week, Salford, Great Britain, 29 March-2 April 2004, pp.419-430.
  • Çambel, E. ve Özgan, E. (2018). Mimari tasarım sürecinde mühendislik sorunlarının mimarlar açısından incelenmesi. Journal of Advanced Technology Sciences. 7(2): 47-70.
  • Çıdık, M. S. (2008). Türk inşaat sektöründeki bilgi yönetimi uygulamalarında yaşanan problemler ve çözüm önerileri. Yayımlanmamış Yüksek Lisans Tezi. İstanbul Teknik Üniversitesi – Fen Bilimleri Enstitüsü – Mimarlık Anabilim Dalı.
  • Elalwani, E. & Çalışkan, E. B. (2024). Integrating BIM technology in construction for effective knowledge management: case studies and methodological insights. Turkish Journal of Engineering, 8 (4), 647-655. Sun, J., Xu, K., ve Chen, P. (2022). IoT-enabled cyber–physical systems for construction worker tracking and safety monitoring. Automation in Construction, 137: 104218.
  • Eşkinat, R. ve Tepecik, F. (2012). İnşaat sektörüne küresel bakış. Afyon Kocatepe Üniversitesi İİBF Dergisi, 14(1): 25-41.
  • Fasoyinu, A. A., Azhar, S., Sattineni, A., Toyin, J. O. (2025). Wearable sensing devices for construction safety: Research trends, applications, challenges, and future opportunities. Automation in Construction, 179, 106424. https://doi.org/10.1016/j.autcon.2025.106424
  • Grünberg, T. (2004). Performance improvement: towards a method for finding and prioritising potential performance improvement areas in manufacturing operations. Emerald; Emerald Group Publıshıng Limited, 52-71, doi:https://doi.org/10.1108/17410400410509969
  • He, Y., He, J., Wen, N. (2023). The challenges of IoT based applications in high risk environments, health and safety industries in the Industry 4.0 era using decision making approach. Journal of Innovation & Knowledge, 8(2), 100347. https://doi.org/10.1016/j.jik.2023.100347 International Organization for Standardization. (2018). ISO 19650-1: Organization and digitization of information about buildings and civil engineering works, including building information modelling (BIM) — Information management using building information modelling — Part 1: Concepts and principles. ISO.
  • Kanan, R., Elhassan, O., Bensalem, R. (2018). An IoT-based autonomous system for workers' safety in construction sites with real-time alarming, monitoring, and positioning strategies. Automation in Construction, volume 88, pages 73-86. https://doi.org/10.1016/j.autcon.2017.12.033
  • Kim, J. H., Jo, B. W., Jo, J. H., & Kim, D. K. (2020). Development of an IoT-based construction worker physiological data monitoring platform at high temperatures. Sensors, 20(19), 5682. https://doi.org/10.3390/s20195682
  • Kim, J., Lee, K., ve Jeon, J. (2024). Systematic literature review of wearable devices and data analytics for construction safety and health. Expert Systems with Applications, 257: 125038. https://doi.org/10.1016/j.eswa.2024.125038
  • Laudon, K. C., ve Laudon, J. P. (2020). Management information systems: Managing the digital firm. Pearson.
  • Li, J., Chen, G., Antwi Afari, M. F. (2024). Recognizing sitting activities of excavator operators using multi sensor data fusion with machine learning and deep learning algorithms. Automation in Construction, 165, Article 105554. https://doi.org/10.1016/j.autcon.2024.105554
  • Ma, J., Li, H., Wang, L., Yu, X., Huang, X. (2025). Multimodal fusion for monitoring worker fatigue in elevated work environments. Advanced Engineering Informatics, 67, 103565. https://doi.org/10.1016/j.aei.2025.103565
  • McKinsey Global Institute. (2017). Reinventing construction: A route to higher productivity. McKinsey & Company.
  • Medori D, Steeple D (2000). A framework for auditing and enhancing performance measurement systems. International Journal of Operations & Production Management, Vol. 20 No. 5 pp. 520–533, doi: https://doi.org/10.1108/01443570010318896
  • Mekruksavanich, S., & Jitpattanakul, A. (2025). Construction worker activity recognition using deep residual convolutional network based on fused IMU sensor data in internet-of-things environment. IoT, 6(3), 36. https://doi.org/10.3390/iot6030036
  • Naranjo, J. E., Mora, C. A., Bustamante Villagómez, D. F., Mancheno Falconi, M. G., Garcia, M. V. (2025). Wearable sensors in ındustrial ergonomics: Enhancing safety and productivity in industry 4.0. Sensors, 25(5), 1526. https://doi.org/10.3390/s25051526
  • Neely, A., Gregory, M., Platts, K., (1995), Performance measurement system design: A literature review and research agenda. International Journal of Operations & Production Management, Vol. 15 Iss: 4, pp. 80 - 116.
  • Sabino, I., Fernandes, M. d. C., Cepeda, C., Quaresma, C., Gamboa, H., Nunes, I. L., Gabriel, A. T. (2024). Application of wearable technology for the ergonomic risk assessment of healthcare professionals: A systematic literature review. International Journal of Industrial Ergonomics, 100, Article 103570. https://doi.org/10.1016/j.ergon.2024.103570
  • Shvets, Y., & Hanák, T. (2023). Use of the Internet of Things in the construction industry and facility management: Usage examples overview. Procedia Computer Science, 219, 1670–1677. https://doi.org/10.1016/j.procs.2023.01.460
  • Shakerian, S., Habibnezhad, M., Ojha, A., Lee, G., Liu, Y., Jebelli, H., Lee, S. (2021). Assessing occupational risk of heat stress at construction: A worker‑centric wearable sensor‑based approach. Safety Science, 142, 105395. https://doi.org/10.1016/j.ssci.2021.105395
  • Slack, N. (2010). Operations management. New York: Financial Times Prentice Hall. ISBN: 9780273731603.
  • Śliwa, D. (2025, March 12). Internet of Things (IoT) in construction: Safety and optimization of construction processes. Univio. https://www.univio.com/blog/internet-of-things-iot-in-construction-safety-and-optimization-of-construction-processes/
  • Tangen, S. (2005). Demystifying productivity and performance. International Journal of Productivity and Performance Management, Vol. 54 No. 1 pp. 34–46, doi: https://doi.org/10.1108/17410400510571437
  • Tecim, V. (2023). YBS Piramidi. https://vahaptecim.com.tr/yonetim-bilisim-sistemleri/, (30.12.2023).
  • Tezcan, Ö., Akcay, C., Sari, M., Cavus, M. (2025). Sensor-Based Automatic Recognition of Construction Worker Activities Using Deep Learning Network. Sensors, 25(13), 3988. https://doi.org/10.3390/s25133988
  • Uğur, L. O. (2006). İnşaat sektöründe riskler ve risk yönetimi – seminer notları. Türkiye Müteahhitler Birliği. http://www.insulaelibertatis.com/ KitaKita/Insaat_Sektorunde_Risk_yonetimi.pdf, (30.12.2023).
  • Uğural, M. N. (2020). İnşaat projelerinde zaman maliyet ödünleşim problemi: Örnek olay analizi. Avrupa Bilim ve Teknoloji Dergisi (19), 460-465. https://doi.org/10.31590/ejosat.726891
  • Wang, J. , Zhu, G. , Wu, S. and Luo, C. (2021) Worker’s helmet recognition and identity recognition based on deep learning. Open Journal of Modelling and Simulation, 9, 135-145. doi: 10.4236/ojmsi.2021.92009.
  • Wang, M., Chen, J., & Ma, J. (2024). Monitoring and evaluating the status and behaviour of construction workers using wearable sensing technologies. Automation in Construction, 165, 105555. https://doi.org/10.1016/j.autcon.2024.105555
  • World Economic Forum & Boston Consulting Group. (2016). Shaping the future of construction: A breakthrough in mindset and technology. World Economic Forum.
  • Yurtcu, A. (2025). Wearable technologies and internet of things for real-time hazard monitoring in occupational health and safety: A systematic content analysis. Innovative Approaches to Engineering Problems, 1(2), 45-54.
Toplam 35 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Bilgi Sistemleri (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Tanzer Genc 0009-0001-5430-9463

Çiğdem Tarhan 0000-0002-5891-0635

Gönderilme Tarihi 28 Ekim 2025
Kabul Tarihi 16 Aralık 2025
Yayımlanma Tarihi 24 Aralık 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 11 Sayı: 2

Kaynak Göster

APA Genc, T., & Tarhan, Ç. (2025). İNŞAAT SEKTÖRÜNDE AKILLI SENSÖR TEKNOLOJİLERİ İLE ÇALIŞAN PERFORMANSININ ÖLÇÜMÜ VE DEĞERLENDİRİLMESİ. Yönetim Bilişim Sistemleri Dergisi, 11(2), 45-62.