2026-04-02
Roziani Maria Gomes, José Maria Franco de Carvalho, Gustavo Henrique Nalon, Hellen Regina de Carvalho Veloso Moura, Flávio Antônio Ferreira, Antônio Cléber Gonçalves Tibiriçá, José Carlos Lopes Ribeiro
The increasing generation of industrial solid waste demands sustainable solutions for its reuse. The literature still lacks studies focused on optimizing the use of eucalyptus wood ash and sewage treatment plant (STP) sludge in cement-lime mortars. This study aims to fill this gap by optimizing the eco-efficiency of cement-lime mortars incorporating these residues, thereby promoting more sustainable practices in the construction industry. A Central Composite Design (CCD) was elaborated to define the mortar compositions to be investigated, along with the evaluation of compressive strength and flexural tensile strength. The statistical desirability function (DSJ) was applied to balance mechanical performance and sustainable resource consumption, seeking the most suitable formulation for the mixtures. Eco-efficiency optimization was conducted under practical masonry workability constraints rather than aiming at maximum mechanical strength without complying with standardized mortar consistency requirements. The results indicated that the lime addition played a predominant role in the mechanical strength of the mortars, whereas the addition of residues showed a less significant effect. The application of the desirability function enabled the identification of the most eco-efficient mortar composition. Regression analyses with coefficients of determination exceeding 80% demonstrated a good correlation between experimental data and the developed mathematical models. These findings contribute to the development of sustainable cementitious materials, demonstrating that the combined use of eucalyptus wood ash and STP sludge in mortar production is technically feasible and environmentally advantageous. This approach promotes the valorization of industrial residues without compromising mortar strength, advancing sustainable construction practices.
DOI: 10.3389/fbuil.2026.17760052026-04-01
Kai Kannan, A. Abdul Rahim
IntroductionThis study focuses on optimizing the performance and sustainability of ternary blended geopolymer mortars (TBMs) incorporating Ground Granulated Blast Furnace Slag (GGBS), metakaolin (MK), and paper sludge ash (PSA). The need for environmentally friendly alternatives to ordinary Portland cement has driven the development of multi-criteria decision-making approaches for sustainable material design.MethodsSixteen mix designs with varying binder proportions and alkali molarities were experimentally evaluated for mechanical, durability, environmental, and economic performance. The Best–Worst Method (BWM) was employed to determine the relative importance of six criteria: workability, compressive strength, water absorption, energy consumption, CO2 emissions, and cost.ResultsResults indicate that increasing MK and PSA contents reduced workability, while balanced GGBS–MK proportions and moderate alkali molarity improved strength. The optimal mix (70% GGBS, 15% MK, 7.5% PSA, 4M NaOH) achieved a compressive strength of 63.67 N/mm2 and water absorption of 1.43%, indicating dense microstructure development. Energy consumption (5.06 MJ/kg) and CO2 emissions (0.54 kg CO2-eq/kg) were significantly lower than conventional OPC mortars. BWM analysis identified compressive strength as the most critical criterion and CO2 emissions as the least significant. The ranking results indicated Mix M15 as the optimal composition.DiscussionThe proposed BWM-based decision-making framework effectively integrates performance, environmental, and economic aspects, providing a practical approach for designing sustainable and high-performance geopolymer mortars.
DOI: 10.3389/fbuil.2026.17487112026-03-30
Jacob Atkins, Donya Hajializadeh, Waqas Iqbal, Farahnaz Soleimani
Traditional fragility-based methods are rigorous, but they can be computationally intensive and difficult to scale to large bridge inventories, particularly when resilience assessments must propagate fragility outputs through functionality and recovery models for time-dependent decision support. This study presents a physics-driven neural surrogate framework that complements fragility-informed workflows by directly predicting a bridge-level seismic resilience index as a continuous system metric. Using pre-1971 concrete box-girder bridges as a case study, we generate a simulation-informed dataset from high-fidelity nonlinear time-history analyses in OpenSees, covering 1,600 bridge-ground motion scenarios. A multilayer perceptron (MLP) model is trained with systematic hyperparameter tuning over loss functions, optimizers, network depth, and regularization. The final MLP achieves over 97% prediction accuracy and outperforms baseline ensemble learning models. By learning directly from physics-based simulations, the proposed surrogate enables rapid and scalable resilience estimation, supporting retrofit prioritization, emergency planning, and resilience-informed design in seismically active regions.
DOI: 10.3389/fbuil.2026.17569082026-03-23
Kunchala Anjaneyulu, S. K. Sekar
This paper analyses the mechanical behaviour and microstructure of Coconut Shell Aggregate Concrete (CSAC) with Silica Fume (SF) as an additional cementitious material. The binder was also partially replaced with SF at 5, 10 and 15 percent and 10 percent of the coarse aggregate replaced with coconut shells (CS). Out of all the mixtures, the one with 10% SF and 10% CS had the highest level of compressive, split tensile strength with an improvement of approximately 15% and flexural strength approximately 5% compared to the reference mix. This performance improvement can mainly be linked to the pozzolanic activity and filler effect of SF that led to the enhanced packing of the particles, increased the density of the matrix, and improved microstructural compactness. X-Ray Diffraction (XRD) was used to establish the greater proportion of amorphous silica, and the generation of secondary calcium silicate hydrate (C-S-H), and provided a qualitative understanding of the densification of the matrix after the introduction of silica fume by SEM analysis. Besides this, various machine learning (ML) algorithms were created; among them, Linear Regression, Support Vector Regression (SVR), Decision Tree, Random Forest, and XGBoost were used to predict the mechanical performance of the mixes, depending on the mixes and curing time. Out of all these models, XGBoost had the highest accuracy in the predicted values (R2 = 0.96, RMSE < 1.5 MPa), which demonstrated the exploratory potential of artificial intelligence for predictive modelling of sustainable lightweight concrete mixtures.
DOI: 10.3389/fbuil.2026.17771272026-03-23
Wolfgang Grenzfurtner, Manfred Gronalt
Reducing the costs of unproductive time in conjunction with the need to reduce rework due to defects and non-conformities is important for improving the efficiency and competitiveness of industrialised housebuilding (IHB), as well as reducing waste. Continuous improvement (CI) of processes and design standards is essential to achieving these goals, and various methods and tools are employed to this end. This paper analyses the suitability of failure mode and effects analysis (FMEA) for supporting CI in IHB. To this end, a case study was conducted within an IHB company to analyse the suitability of the method employed in this specific industry. FMEA was assessed using data generated through participant observation, qualitative interviews, workshop protocols, and the output of its application: the revised processes and quality costs from two case studies. The findings of the case study showed the effects of involving experts’ knowledge and considering employees’ workplace-specific knowledge in a CI process. The assessment of potential failures was found to be more evidence-based than in the initial situation and resulted in cost effective solutions from a quality cost analysis perspective. Overall, FMEA offers the opportunity to improve the efficiency of CI programmes in IHBs but managers need to be aware of its weaknesses. This paper provides three contributions: (1) Using a real-world application, it shows how FMEA can be embedded as a structured, team-based risk analysis in the IHB order fulfilment process (OFP) and what measurable quality cost effects result from this. (2) It highlights the knowledge integration function of FMEA in the construction context. (3) It discusses organisational learning as a channel of impact (cooperation, communication, shared process view) and identifies methodological limitations of the application (e.g., participation effort). Together, this addresses the research gap in the systematic, evidence-based use of FMEA for CI programmes in IHB.
DOI: 10.3389/fbuil.2026.17846422026-03-23
Stavroula Sigourou, Panayiotis Dimitriadis, Vasiliki Pagana, Alexia Tsouni, Theano Iliopoulou, G.-Fivos Sargentis, Romanos Ioannidis, Dimitra Dimitrakopoulou, Efthymios Chardavellas, Nikos Mamassis, Demetris Koutsoyiannis, Charalampos (Haris) Kontoes
Flood hazard assessment—together with vulnerability and risk analysis—is closely linked to flood resilience and has been extensively studied in densely populated areas, where the most catastrophic floods tend to occur. The need for a holistic and transferable methodology is critical considering that, different simulation approaches are often used, while key methodological phases are sometimes omitted. Within the framework of the Programming Agreement of the Prefecture of Attica, the BEYOND Centre (IAASARS/NOA), in cooperation with the NTUA research group have developed the methodology presented in this work. The methodology was implemented at high spatial resolution in five flood-affected river basins in Attica, with the Pikrodafni River basin being presented in detail in this study. Data acquisition constituted a core component of the methodology and involved targeted spatial datasets, Earth-observation imagery, time-series data, historical flood records, and relevant prior studies obtained from the competent authorities. Field visits were conducted to characterize site conditions and verify the collected datasets, identifying high-risk critical points, and measuring the dimensions of hydraulic structures (bridges, culverts) and channel properties. Regarding modeling, design-flood scenarios with typical return periods were analyzed in accordance with the Directive 2007/60/EC. HEC-HMS was used to generate hydrographs for each sub-basin, which were then imported into the quasi-2D LISFLOOD-FP model as a means to prepare and calibrate the HEC-RAS model, where a rain-on-grid methodology integrated the hydrologic and hydraulic flood processes at the area of interest. High spatial resolution was maintained throughout, with particular emphasis on uncertainty analysis and on the detailed representation of infrastructure and urban areas, given their strong influence on flood dynamics. Results indicate that overflow typically occurs in buried streams, along adjacent roads in the downstream reach of the river, at stream confluences, and at the upstream inlet where natural streams enter the drainage pipe network. Up to 200 critical points were identified, of which up to 35% were classified as first-priority sites for intervention.
DOI: 10.3389/fbuil.2026.1768439