Yapay Zekânın Yapı Sektöründe Yaşam Döngüsü Değerlendirmesine Entegrasyonu: Bibliyometrik ve Eleştirel Bir İnceleme

Abstract

Binaların çevresel etkilerindeki artış, sürdürülebilirlik değerlendirme araçlarının kapsam ve güvenilirliğine yönelik ihtiyacı belirginleştirmiş; bu bağlamda Yaşam Döngüsü Değerlendirmesi (LCA), çevresel performansın nicel analizinde merkezi bir konuma yerleşmiştir. Yapay zekâ (YZ), LCA süreçlerinin doğruluğunu, verimliliğini ve otomasyon potansiyelini artırmada stratejik bir araç olarak öne çıkmaktadır. Bu çalışma, inşaat sektöründe YZ entegreli LCA araştırmalarına yönelik güncel ve eleştirel bir inceleme sunmaktadır. Çalışmada, Web of Science ve Scopus veri tabanlarından elde edilen 883 yayının bibliyometrik analizi ile YZ tekniklerinin LCA iş akışlarına açıkça entegre edildiği, metodolojik şeffaflığa sahip 18 seçilmiş makalenin sistematik değerlendirmesi yürütülmüştür. Bulgular, makine öğrenmesi ve yapay sinir ağlarının enerji tüketimi ve karbon emisyonu tahminlerinde yaygın kullanıldığını göstermektedir. Ancak YZ-LCA entegrasyonu; yapısal olmayan veriler, standart protokol eksikliği, düşük araç uyumluluğu ve kaliteli veriye kısıtlı erişim nedeniyle henüz bütüncül bir yapıya ulaşamamıştır. Mevcut literatür çoğunlukla operasyonel enerjiye odaklanıp, gömülü etkileri ve geniş sürdürülebilirlik göstergelerini ihmal etmektedir. Gelecek araştırmalar standart veri şemaları, gerçek zamanlı izleme ve vaka temelli doğrulamalar içeren YZ çerçevelerine öncelik vermelidir. Bu entegrasyon, yapı sektöründe veriye dayalı, şeffaf ve uyarlanabilir sürdürülebilirlik stratejileri için dönüştürücü potansiyel taşımaktadır. 

Keywords

• Yapılar
• Yaşam döngüsü değerlendirmesi
• Yapay zeka
• Bibliyometrik analiz
• Sistematik analiz

INTEGRATING ARTIFICIAL INTELLIGENCE INTO LIFE CYCLE ASSESSMENT IN THE BUILDING INDUSTRY: A BIBLIOMETRIC AND CRITICAL REVIEW

Abstract

Increasing environmental impacts of buildings necessitate robust sustainability assessment tools, positioning Life Cycle Assessment (LCA) as a central methodology for evaluating environmental performance. Artificial Intelligence (AI) offers strategic potential to enhance the accuracy, efficiency, and automation of LCA processes. This study critically reviews AI-integrated LCA research in the construction sector. A bibliometric analysis of 883 publications from Web of Science and Scopus was conducted, alongside a systematic review of 18 articles explicitly integrating AI into LCA workflows. Findings show Machine Learning (ML) and Artificial Neural Networks (ANN) are predominantly used to predict energy consumption and carbon emissions. However, AI-LCA integration remains fragmented due to unstructured data, lack of standardized protocols, low interoperability, and restricted access to high-quality datasets. Current literature primarily focuses on operational energy use, largely neglecting embodied impacts and broader sustainability indicators. Future research should prioritize AI frameworks incorporating standardized data schemas, real-time monitoring, and case-based validation. Integrating AI into LCA offers transformative potential for data-driven, transparent, and adaptable sustainability strategies in construction.

Keywords

• Buildings
• Life cycle assessments
• Artificial intelligence
• Bibliometric analysis
• Systematic analysis

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