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Publication date: 19 December 2024

Solomon Oyebisi, Mahaad Issa Shammas, Reuben Sani, Miracle Olanrewaju Oyewola and Festus Olutoge

The purpose of this paper is to develop a reliable model that would predict the compressive strength of slurry infiltrated fiber concrete (SIFCON) modified with various…

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Abstract

Purpose

The purpose of this paper is to develop a reliable model that would predict the compressive strength of slurry infiltrated fiber concrete (SIFCON) modified with various supplementary cementitious materials (SCMs) using artificial intelligence approach.

Design/methodology/approach

This study engaged the artificial intelligence to predict the compressive strength of SIFCON through deep neural networks (DNN), artificial neural networks, linear regression, regression trees, support vector machine, ensemble trees, Gaussian process regression and neural networks (NN). A thorough data set of 387 samples was gathered from relevant studies. Eleven variables (cement, silica fume, fly ash, metakaolin, steel slag, fine aggregates, steel fiber fraction, steel fiber aspect ratio, superplasticizer, water to binder ratio and curing ages) were taken as input to predict the output (compressive strength). The accuracy and reliability of the developed models were assessed using a variety of performance metrics.

Findings

The results showed that the DNN (11-20-20-20-1) predicted the compressive strength of SIFCON better than the other algorithms with R2 and mean square error yielding 95.89% and 8.07. The sensitivity analysis revealed that steel fiber, cement, silica fume, steel fiber aspect ratio and superplasticizer are the most vital variables in estimating the compressive strength of SIFCON. Steel fiber contributed the highest value to the SIFCON’s compressive strength with 16.90% impact.

Originality/value

This is a novel technique in predicting the compressive strength of SIFCON optimized with different SCMs using supervised learning algorithms, improving its quality and performance.

Details

World Journal of Engineering, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1708-5284

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Article
Publication date: 30 December 2024

Ravita Kharb, Charu Shri, Neha Saini and Dinesh Kumar

Green and digital transitions (twin transition) help in achieving climate neutrality, cutting down the pollution level and reviving biodiversity. Organizations can mitigate their…

95

Abstract

Purpose

Green and digital transitions (twin transition) help in achieving climate neutrality, cutting down the pollution level and reviving biodiversity. Organizations can mitigate their ecological footprint, improve their digital portfolio and fulfil their ethical responsibilities by fostering a sustainable global community and adhering to a well-defined plan. In such backdrop, this study aims to propose strategies by identifying the strengths, weaknesses, opportunities and threats of the twin transition and to categorize and evaluate the internal and external factors for achieving environmental sustainability.

Design/methodology/approach

Following a comprehensive review of the literature, 27 factors have been identified and analyzed using the strengths, weakness, opportunities and threat matrix (SWOT). Threats, opportunities, weakness, strength matrix analysis approach is used to limit risk, overcome weaknesses and take advantage of opportunities. Effective strategies are designed and implemented using strategic planning action and evaluation matrix (SPACE) and quantitative strategic planning (QSPM) approach.

Findings

The study found that aggressive strategies considering economic strength and global opportunities must be used to accomplish green growth objectives. Out of three strategies, the focus on green technological innovation by taking advantage of financial mechanisms at the international level is highly recommended to achieve environmental sustainability.

Research limitations/implications

Through promoting green and digital transformation, the current study provides insightful policy recommendations for economic development. It may help to improve operational efficacy and to obtain a competitive edge by successfully limiting their adverse environmental effects in the global market through the implementation of twin transition. The process of developing a strategy aids stakeholders, including society, workers and other parties, in giving environmental sustainability a top priority while making financial decisions.

Social implications

Strategy development helps society, employees and all the other stakeholders to prioritize their green and digital transition decisions for environmental sustainability.

Originality/value

The current study identifies the factors formulating strategies using SWOT, TOWS, SPACE and QSPM matrix analysis for achieving environmental sustainability in the pervasive twin transition.

Details

Sustainability Accounting, Management and Policy Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2040-8021

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Article
Publication date: 13 February 2025

Aliyyu Abiola Abdulraheem, Agboola Shamsudeen Abdulazeez, Abdulkabir Opeyemi Bello, Mustapha Yusuf Faruq, Abdulhakeem Kolawole Musa and Moshood Olawale Shabi

This study aims to explore the factors influencing the quality of project delivery in the Nigerian construction industry, addressing a critical gap in the existing literature and…

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Abstract

Purpose

This study aims to explore the factors influencing the quality of project delivery in the Nigerian construction industry, addressing a critical gap in the existing literature and providing insights for improving construction practices in Nigeria.

Design/methodology/approach

A mixed-methods approach was employed, combining quantitative analysis of survey data with qualitative insights from semi-structured interviews. The survey gathered data on various factors affecting project quality, while interviews provided contextual understanding and nuanced perspectives from industry professionals.

Findings

This study identifies seven key factors influencing project delivery quality: contractor performance (β = 0.235, p < 0.001), design and materials management (β = 0.180, p < 0.001), documentation and procurement (β = 0.175, p < 0.001), workforce management and skills (β = 0.142, p < 0.001), project management practices (β = 0.153, p < 0.001), budget and financial management (β = 0.139, p < 0.001) and government regulations (β = 0.080, p < 0.001). Contractor performance and design/materials management have the most significant impact, underscoring the need for strong contractor supervision, material standards and efficient design processes. Additionally, effective documentation and procurement are crucial for success, with procurement inefficiencies leading to delays and reduced quality. Workforce competencies also play a major role, as inadequate training and a lack of skilled labour result in construction defects and compromised project outcomes.

Research limitations/implications

Limitations of the study include its focus primarily on industry professionals' perspectives and its limited scope to the Nigerian context. Future research could explore additional stakeholder perspectives and extend the study to other geographical contexts.

Practical implications

The findings offer valuable insights for construction professionals, policymakers, and other stakeholders, informing the development of a tailored approach to enhance quality project delivery in Nigeria. By addressing critical factors identified in the study, stakeholders can mitigate risks, improve project outcomes and contribute to the sustainable development of the construction industry.

Originality/value

This study contributes to the existing literature by comprehensively analysing the factors influencing quality project delivery in the Nigerian construction industry. Integrating quantitative and qualitative data provides a nuanced understanding of the challenges and opportunities inherent in ensuring project quality, offering practical implications for industry practitioners and policymakers.

Details

The TQM Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1754-2731

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