AI-Enabled Sustainable Project Management in Construction: Predictive Analytics for Schedule and Resource Optimisation | Chapter 8 | Engineering Research: Perspectives on Recent Advances Vol. 10

 

This chapter introduces a Sustainable Project Management in Construction framework that operationalises artificial intelligence to (i) anticipate schedule slippages, (ii) optimise labour and material deployment, and (iii) embed sustainability indicators directly into project decision-making. Using records from 33 projects spanning infrastructure, residential, and commercial sectors, we develop and validate a Random Forest Regressor benchmarked against alternative machine-learning models to forecast schedule overruns and resource utilisation. A 10-fold cross-validation protocol, residual diagnostics, and scenario analyses demonstrate robust predictive performance for the delay prediction: R²≈0.87, MSE≈11.8 days; resource utilisation: R²≈0.82 without degrading when sustainability features are included. We describe a practitioner-oriented, Python-based interface that ingests site data, automates preprocessing, and returns interpretable forecasts alongside waste-reduction and recycled-content metrics to support “what-if” analyses. Governance measures anonymisation, role-based access, and compliance with institutional protocols, are integrated to protect stakeholders and sustain data trust. The chapter situates SPMC within current literature and practice, distilling design choices and implementation patterns that readers can adapt to heterogeneous project contexts. Limitations and future directions are discussed, including expanding the dataset’s diversity, coupling with IoT data streams for real-time sensing, and integrating life-cycle assessment to widen coverage of carbon and circularity indicators. Collectively, SPMC provides a replicable pathway for aligning cost-time performance with measurable environmental outcomes in contemporary construction management.

 

 

Author(s) Details

Mohamed Y. Laissy
Department of Civil Engineering, University of Prince Mugrin, Medina, Saudi Arabia.

 

Omar Mostafa Dakhil
Department of Architecture Engineering, University of Prince Mugrin, Medina, Saudi Arabia.

 

Please see the book here :- https://doi.org/10.9734/bpi/erpra/v10/6163

Sustainable Optimization for Innovative Practices of Smart Buildings with the AI Based Deep Learning Techniques | Chapter 3 | Research Updates in Mathematics and Computer Science Vol. 7

 

A major driver in the rethinking of sustainable engineering and management practices is artificial intelligence (AI), especially in the use of cutting-edge deep learning techniques. The primary focus of this study is to assess the efficacy of Long Short-Term Memory (LSTM) networks as an example of the substantial role that artificial intelligence (AI) plays. When it comes to improving operational performance, optimizing resource allocation, and reducing environmental implications, the Long Short-Term Memory (LSTM) model—a complex sort of recurrent neural network—is crucial. An interesting case study illustrating the use of LSTM algorithms to optimize smart building energy usage in real time is included in this research. By utilizing LSTM for comprehensive pattern analysis and making real-time adjustments, AI exhibits impressive efficiency gains in reducing energy waste. Within the broader context of sustainable engineering, this study demonstrates the effectiveness and efficiency of Long Short-Term Memory (LSTM), therefore contributing significantly to the development of a resilient and ecologically conscious future.

 

Author(s) Details

 

K. Rajendra Prasad

Department of CSE (CS), Institute of Aeronautical Engineering, Dundigal, Hyderabad, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/rumcs/v7/452

Sustainable Optimization for Innovative Practices of Smart Buildings with the AI Based Deep Learning Techniques | Chapter 4 | Research Updates in Mathematics and Computer Science Vol. 7

 

A major driver in the rethinking of sustainable engineering and management practices is artificial intelligence (AI), especially in the use of cutting-edge deep learning techniques. The primary focus of this study is to assess the efficacy of Long Short-Term Memory (LSTM) networks as an example of the substantial role that artificial intelligence (AI) plays. When it comes to improving operational performance, optimizing resource allocation, and reducing environmental implications, the Long Short-Term Memory (LSTM) model—a complex sort of recurrent neural network—is crucial. An interesting case study illustrating the use of LSTM algorithms to optimize smart building energy usage in real time is included in this research. By utilizing LSTM for comprehensive pattern analysis and making real-time adjustments, AI exhibits impressive efficiency gains in reducing energy waste. Within the broader context of sustainable engineering, this study demonstrates the effectiveness and efficiency of Long Short-Term Memory (LSTM), therefore contributing significantly to the development of a resilient and ecologically conscious future.

 

Author(s)details:-

 

K. Rajendra Prasad (Professor)
Department of CSE (CS), Institute of Aeronautical Engineering, Dundigal, Hyderabad, India.

 

Please See the book here :- https://doi.org/10.9734/bpi/rumcs/v7/452