Towards Sustainable Construction: Experimental and Machine Learning-Based Analysis of Wastewater-Integrated Concrete Pavers

Nosheen Blouch, Syed Noman Hussain Kazmi, Mohamed Metwaly (), Nijah Akram, Jianchun Mi and Muhammad Farhan Hanif ()
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Nosheen Blouch: Department of Building and Architectural Engineering, FE&T, Bahauddin Zakariya University, Multan 60000, Pakistan
Syed Noman Hussain Kazmi: Department of Civil Engineering, FE&T, Bahauddin Zakariya University, Multan 60000, Pakistan
Mohamed Metwaly: Archaeology Department, College of Tourism and Archaeology, King Saud University, P.O. Box 2627, Riyadh 12372, Saudi Arabia
Nijah Akram: Department of Architectural Engineering Technology, Punjab Tianjin University of Technology, Lahore 54590, Pakistan
Jianchun Mi: Department of Energy & Resource Engineering, College of Engineering, Peking University, Beijing 100871, China
Muhammad Farhan Hanif: Department of Energy & Resource Engineering, College of Engineering, Peking University, Beijing 100871, China

Sustainability, 2025, vol. 17, issue 15, 1-35

Abstract: The escalating global demand for fresh water, driven by urbanization and industrial growth, underscores the need for sustainable water management, particularly in the water-intensive construction sector. Although prior studies have primarily concentrated on treated wastewater, the practical viability of utilizing untreated wastewater has not been thoroughly investigated—especially in developing nations where treatment expenses frequently impede actual implementation, even for non-structural uses. While prior research has focused on treated wastewater, the potential of untreated or partially treated wastewater from diverse industrial sources remains underexplored. This study investigates the feasibility of incorporating wastewater from textile, sugar mill, service station, sewage, and fertilizer industries into concrete paver block production. The novelty lies in a dual approach, combining experimental analysis with XGBoost-based machine learning (ML) models to predict the impact of key physicochemical parameters—such as Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), and Hardness—on mechanical properties like compressive strength (CS), water absorption (WA), ultrasonic pulse velocity (UPV), and dynamic modulus of elasticity (DME). The ML models showed high predictive accuracy for CS (R 2 = 0.92) and UPV (R 2 = 0.97 direct, 0.99 indirect), aligning closely with experimental data. Notably, concrete pavers produced with textile (CP-TXW) and sugar mill wastewater (CP-SUW) attained 28-day compressive strengths of 47.95 MPa and exceeding 48 MPa, respectively, conforming to ASTM C936 standards and demonstrating the potential to substitute fresh water for non-structural applications. These findings demonstrate the viability of using untreated wastewater in concrete production with minimal treatment, offering a cost-effective, sustainable solution that reduces fresh water dependency while supporting environmentally responsible construction practices aligned with SDG 6 (Clean Water and Sanitation) and SDG 12 (Responsible Consumption and Production). Additionally, the model serves as a practical screening tool for identifying and prioritizing viable wastewater sources in concrete production, complementing mandatory laboratory testing in industrial applications.

Keywords: wastewater reuse; sustainable construction; machine learning; XGBoost modeling; environmental sustainability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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