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

Random Forest Analysis of Cassava Mosaic Disease Factors: Impacts on Livelihoods of Cassava Farmers | Chapter 4 | Geography, Earth Science and Environment: Research Highlights Vol. 3

Cassava mosaic disease (CMD) is a significant threat to cassava production in various regions, particularly in Africa. The disease is caused by several geminiviruses, including African cassava mosaic virus (ACMV), East African cassava mosaic virus (EACMV), and East African cassava mosaic Cameroon virus (EACMCMV). This study aimed to identify key CMD-related factors affecting Cameroon cassava farmers’ incomes originating from both the sale of cassava cuttings (V215) and the sale of cassava roots (V216). The dataset had 630 records originating from respondents residing within 4 regions of Cameroon – Adamawa, Center, East and South. Here, nine CMD-related variables were used to independently train two Random Forest models. Random forest residual plots are a crucial tool in model exploration and diagnostics. These models were later employed for regression-based prediction of both financial targets V215 and V216. The Random Forest (RF)-based mean absolute percentage error for targets V215 and V216 were 0.19 and 1.25 respectively. The RF-based mean Gaussian deviance for targets V215 and V216 were 0.07 and 0.51 respectively. Based on RF feature importance scores (RFFI), the top 3 factors affecting income originating from the sale of cassava cuttings were found to be: late appearance of symptoms as a difficulty associated with regular field monitoring (RFFI of 0.2594), removal of infected plants as a method of controlling frequent occurrence of viral diseases in respondents’ cassava fields (RFFI of 0.1633) and lack of healthy planting material due to frequent occurrence of viral diseases in respondents’ cassava fields (RFFI of 0.1495). Also, the top 3 factors affecting income originating from the sale of cassava roots were found to be: the replacement of infected plants with healthy cuttings as a method of controlling the frequent occurrence of viral diseases in respondents’ cassava fields (RFFI of 0.1974), decrease in yield due to frequent occurrence of viral diseases in respondents’ cassava fields (RFFI of 0.1530) and poor plant growth due to frequent occurrence of viral diseases in respondents’ cassava fields (RFFI of 0.1388). This study has shown that the continuous reliance of cassava farmers on vegetative propagation-based cassava planting material is principally responsible for the bulk of the financial losses they experience, and so an adoption of seed-based cassava planting material would go a long way to secure their on-farm livelihood in cassava production.

 

Author (s) Details

Dickmi Vaillam Claudette
Faculty of Agronomy and Agricultural Sciences, University of Dschang, P.O. Box 96, Dschang, West Region, Cameroon.

 

Tchouamo Isaac Roger
Faculty of Agronomy and Agricultural Sciences, University of Dschang, P.O. Box 96, Dschang, West Region, Cameroon.

 

Please see the book here:- https://doi.org/10.9734/bpi/geserh/v3/3312

Analysis of Variations in PM2.5 Concentration and Meteorological Factors in Harbin | Chapter 9 | Emerging Issues in Environment, Geography and Earth Science Vol. 9

 The combustion of fossil fuels has led to increasingly prominent air pollution problems. A major air pollutant, PM2.5, has been gradually tackled around the world. This study investigated the relationship between PM2.5, a number of influencing factors, and their temporal changes using a machine-learning method in Harbin from 2015 to 2019. It can be seen from the analysis that the random forest model has good performance in predicting PM2.5 concentration. In this model, the mean Relative humidity and aerosol optical depth have a high impact on PM2.5 concentration, but there was negligent correlation with PM2.5. The results indicated that the level of PM2.5 pollution continuously decreased from 2015 to 2019, and there were significant seasonal differences in PM2.5 concentration and its variations. There were significant seasonal differences in the PM2.5 concentration over the past five years. The PM2.5 concentration was highest in winter but declined in a wavelike pattern, slightly lower in autumn, declining in a cliff-like manner, low but stable in spring, and lowest in summer, decreasing gradually year by year.  In 2019, due to the impact of heating and adverse meteorological conditions, PM2.5 pollution during the heating period increased significantly. This study provides theoretical and data support for the analysis of PM2.5 pollution in Harbin and formulation of air pollution control policies. Due to the different accuracy of the different data, this article can only be used for calculation, which may cause deviation of the analysis results. In future work, we will continue to improve this issue.

Author(s) Details:

Xiuyan Gao,
School of Energy Science and Engineering, Harbin Institute of Technology, 92 West Dazhi Street, Harbin 150001, China.

Yuan Yuan,
School of Energy Science and Engineering, Harbin Institute of Technology, 92 West Dazhi Street, Harbin 150001, China.

Please see the link here: https://stm.bookpi.org/EIEGES-V9/article/view/13930

A Comparative Study of Machine Learning Models for Heart Disease Prediction | Chapter 3 | Advances and Challenges in Science and Technology Vol. 2

 Heart disease persists to be a major all-encompassing public health issue, contributing to infinite deaths and disabilities. Effective deterrent interventions and individualized situation programs depend on timely and exact risk prediction of myocardial infarction. Significant advancements in the field of cardiac affliction prediction have been created thanks to the development of machine intelligence techniques. This book chapter offers a all-encompassing analysis of the strengths, imperfections, and overall effectiveness of the various machine intelligence models used for heart disease indicator.

Author(s) Details:

Sunanda Budihal,
Department of Computer Science, Shah Sogamal Peeraji Oswal Government First Grade College, Muddebhihal, Vijayapura, Karnataka, India.

Sheetalrani Rukmaji Kawale,
Department of Computer Science, Karnataka State Akkamahadevi Women University, Vijayapura, Karnataka, India.

Aparna Atul Junnarkar,
Department of Information Technology, Vishwakarma Institute of Information Technology (VIIT), Pune, Maharashtra, India.

H. Faritha Begam,
Seethalakshmi Achi College for Women, Pallathur 630 107, Tamilnadu, India.

Girish M.,
Department of Computer Science and Engineering, Gopalan College of Engineering and Management, Bangalore, Karnataka, India.

Please see the link here: https://stm.bookpi.org/ACST-V2/article/view/11926

Intelligent Techniques for Prediction of Kharif Food Grains Yield in Gujarat| Chapter 11 | Research Highlights in Agricultural Sciences Vol. 9

 Machine learning is a lively decision support finish for crop yield prediction. Yield prognosis could be a very important land issue. Any farmer is curious about knowing what pile of yield he's getting ready for. In the past, yield guess was performed by seeing farmers' expertise in particular fields and crops. Based on previous dossier, we are able to think crop yield victimization utilizing a machine-knowledge technique. Crop yield guess is an important area of research that helps in guaranteeing food freedom all around the planet. Developing higher methods to predict crop output in several climates can assist farmers and different stakeholders in making main decisions in conditions of scientific farming and crop choice. This paper reports on the utilization of diversified linear regression (MLR), Gaussian Process, SMOreg and chance forest to think Kharif food seed crop yield for Gujrat state, India. The parameters preferred for the study were precipitation, minimum hotness, average temperature, maximum hotness, reference crop evapotranspiration, area, result, and yield for the Kharif season (June to November) for the years 2000 to 2020. The dataset was treated victimisation the WEKA tool.

Author(s) Details:

Patadiya Jaykin,
Smt. Chandaben Mohanbhai Patel Institute of Computer Applications, CHARUSAT, Changa, Gujarat, India.

Mittal Desai,
Smt. Chandaben Mohanbhai Patel Institute of Computer Applications, CHARUSAT, Changa, Gujarat, India.

Dip Patel,
Smt. Chandaben Mohanbhai Patel Institute of Computer Applications, CHARUSAT, Changa, Gujarat, India.

Bhargav Vayas.,
Smt. Chandaben Mohanbhai Patel Institute of Computer Applications, CHARUSAT, Changa, Gujarat, India.

Please see the link here: https://stm.bookpi.org/RHAS-V9/article/view/9477

Evaluation of Data Science Algorithm Using Prediction System: Government Schemes in Rural Sectors | Chapter 3 | Advanced Aspects of Engineering Research Vol. 12

 Each dataset is then imported into the Eunectes murinus Navigator at that moment. The examination and aggregation of the understudies' networks (SC, ST, BC, and MBC) and thus the plans are then carried out. Then, using forecasting algorithms (Nave Bayes, Random Forest, and Support Vector Machine (SVM)), what are the plans that the understudy is qualified for are predicted. The accuracy of the three computations is the basis of an investigation. The accuracy of the three calculations is lowered, and the effective calculation that produces the most accurate end result is finally used to play out the forecast of the plans that a certain understudy is qualified for. Finally, the understudies are given the anticipated plans predicted using the most elevated effective calculation among the three computations. As a result of this attempt, country understudies will be able to return to consider completely various recipient plans provided by the government, which they will employ for the advancement of the country's environmental elements.

Author (s) Details

Dr. S. Maheswari
National Engineering College, Kovilpatti, Tamil Nadu, India.


M. Manochitra
National Engineering College, Kovilpatti, Tamil Nadu, India.

D. Thamarai Selvi
National Engineering College, Kovilpatti, Tamil Nadu, India.

View Book :-  https://stm.bookpi.org/AAER-V12/article/view/1280