Journal of Science and Technology in Civil Engineering (JSTCE) - HUCE

2026-09-28T02:19:08+00:00

Durability and structural integrity of nuclear power plant concrete: A critical review of materials, ageing, and assessment

2026-09-25T09:57:18+00:00

Multiple components in nuclear power plants are made of concrete, which provide structural support, confinement, and radiation shielding over multi-decadal service. This critical review synthesizes evidence on material selection, irradiation and internal swelling, thermal and chemical degradation, mechanical-property evolution, non-destructive assessment, structural health monitoring, and long-term operation frameworks, with particular attention to evidence transferability and coupled ageing. Stiffness and tensile-related properties generally respond to distributed cracking earlier than compressive strength. This pattern is evident across historical irradiation datasets, experimental degradation studies, operating experience, and structural analyses. However, structural significance depends strongly on aggregate mineralogy, temperature and moisture history, restraint, prestress, exposure gradients, and the credited safety function. The available evidence does not support a single universal irradiation or degradation threshold that can be applied without regard to aggregate mineralogy, exposure conditions and structural restraint. This review introduces a coupled-ageing convergence framework that maps diverse mechanisms (irradiation, alkali-silica reaction (ASR), delayed ettringite formation (DEF), thermal, chemical, corrosion) onto six structural state variables (dimensional change, cracking, stiffness/tensile degradation, transport, prestress evolution, embedded-steel deterioration) and demonstrates, through integrated evidence synthesis, that compressive strength alone is an incomplete condition indicator for long-term operation (LTO) assessment. A defensible long-term operation strategy therefore combines traceable material and exposure records, targeted multi-method inspection, selective destructive verification, uncertainty-aware structural analysis, and function-specific acceptance criteria.

A sequential computational framework for recycled-material 3D printing concrete: From pumping flow to buildability limit

2026-09-25T09:57:20+00:00

Recycled sand is increasingly used as a sustainable substitute for natural fine aggregate in 3D printing concrete
(3DPC), yet the ultra-fine fraction generated during recycling, recycled concrete crushed fine (RCCF, < 63 µm),
remains underexploited as a partial cement substitute. Existing studies on this material are limited to experimental characterisation, without extending to computational prediction of behaviour throughout the printing process. This study addresses that gap by developing a sequential Computational Fluid Dynamics (CFD) and Finite Element Method (FEM) framework to evaluate an optimised mortar mix incorporating recycled sand
and 10% RCCF (mix FIN 10). The fresh-state flow within the pumping pipe was simulated in OpenFOAM using a Bingham model and validated against nine physical printing trials, with an average deviation of 22% in volumetric flow rate. The post-extrusion structural response was then simulated in CalculiX using a layer-wise activation scheme and a Mohr–Coulomb plasticity model calibrated from fresh-state compression and direct shear tests. The simulation showed that the printed wall could be successfully built up to a height of 540 mm (36 layers) without collapse, reaching a numerically predicted buildability limit of 555 mm at layer 37. The results demonstrate the feasibility of RCCF as a cement substitute and establish an experimentally calibrated, process-scale computational methodology linking pumping flow to post-deposition structural stability for this recycled-material 3DPC mix.

Long-term mechanical properties and durability of a biological coating mortar using Rhodobacter capsulatus PN7 immobilized in vermiculite

2026-09-25T09:57:20+00:00

This study evaluates the influence of vermiculite immobilizing the indigenous purple nonsulfur bacterium Rhodobacter capsulatus PN7 on the long-term mechanical performance and selected durability-related properties of a cementitious coating mortar intended for sewer concrete structures. PN7 produces extracellular polymeric substances (EPS)/glycocalyx, forms biofilms and oxidizes sulfide. Short-term tests confirmed that the strain remained viable after 72 h of mortar hardening and retained sulfide-oxidizing activity. Three mortar systems were investigated: vermiculite without bacteria (V), vermiculite immobilizing the Korean bacterial strain (V-HQ), and vermiculite immobilizing the Vietnamese PN7 strain (V-VN). Vermiculite replaced 0, 10, 20, and 30 vol.% of the fine aggregate. Flexural and compressive strengths were measured at 28, 360, and 720 days under three curing/exposure conditions. Capillary water absorption and the total electrical charge passed were determined as transport-related indicators of water uptake and resistance to ionic/chloride ingress. V-vermiculite progressively reduced mortar strength as its content increased. In contrast, PN7-immobilized
vermiculite mitigated part of the strength loss, with the effect becoming more evident at later ages. The relative performance of V-VN and V-HQ was not consistent across all mixtures and exposure conditions. The 30% V-VN mixture exhibited the lowest capillary water uptake, whereas 20% V-VN provided a more balanced combination of mechanical performance and durability. The results support the potential of PN7-immobilized vermiculite for biological coating applications; however, direct resistance to H2S-induced or biogenic sulfuricacid deterioration and long-term bacterial persistence require further verification.

Predicting compressive creep of SCC based on model B4TW-SCC: Optimization method and experimental

2026-10-06T08:30:21+00:00

Many prediction models have been developed to address the disadvantages of creep, which is induced by a
constant load on concrete structures, such as cracks, deflections, serviceability failures, and reduced long-term
performance. However, the available models still lack accuracy in predicting the compressive creep of concrete,
particularly in the case of self-compacting concrete (SCC), which differs from conventional concrete in its
composition. Therefore, the model B4TW-SCC was calibrated using the B4 model to meet the SCC database
requirements. The optimization of the new prediction model was carried out by evaluating statistical factors and
the weighting plan, which subdivided the 266 test curves in the laboratory database into independent variables.
The developed prediction model performed better than the other models, based on both the SCC database and
experimental results in this study, which were conducted on the creep behaviour of SCC containing fly ash in
different contents, 20% and 40% by mass of cementitious material.

Unified multi-material topology optimization of heterogeneous structures for three-phase multi-physics systems

2026-09-25T09:57:21+00:00

While structural topology optimization is a useful tool for multiphysics designs, most existing studies focus on
homogeneous material configurations, with limited attention given to heterogeneous systems. To address this
limitation, this paper proposes a unified topology optimization framework for heterogeneous structures - specifically Functionally Graded Materials (FGMs) - under triply coupled thermal, mechanical, and design-dependent pressure loads. Unlike traditional discrete multi-material approaches, the proposed methodology utilizes the continuous spatial variation of material properties using an explicit power-law interpolation scheme. The model integrates a sequential thermo-mechanical coupling strategy with a Darcy-based representative solid-phase model to evaluate design-dependent pressure fields. This allows for a consistent treatment of the interactions among thermal effects, mechanical responses, and evolving pressure loads. Several numerical examples are presented to verify the accuracy and efficiency of the proposed approach. Furthermore, a comparison demonstrates that FGM designs provide improved structural performance over conventional homogeneous layouts in three-phase multiphysics environments. This shows their applicability in engineering practice

Experimental and semi-analytical investigation of dynamic behavior of sandwich plates with porous core and CFRP face sheets

2026-09-25T09:57:21+00:00

This paper presents an experimental and theoretical investigation of the vibration behavior of sandwich plates consisting of an Expanded Polystyrene (EPS) porous concrete core and carbon fiber-reinforced polymer (CFRP) face sheets. For the theoretical study, Reddy’s third-order shear deformation theory (RTSDT), combined with the pb-2 Ritz method, is employed to formulate the governing equations, from which natural frequencies are obtained via eigenvalue analysis and dynamic responses are computed using the Newmark integration scheme. The numerical model is validated against established benchmarks. For the experimental study, rectangular sandwich plate specimens with CFRP layups are fabricated and tested under two opposite edges simply supported and the remaining two edges free (SFSF) boundary conditions. The fundamental natural frequencies are measured using step-release free vibration tests with five accelerometers and Fast Fourier Transform (FFT) analysis. The experimentally obtained natural frequencies are then compared with the semi-analytical predictions. The close correlation between the two approaches confirms the reliability of both the proposed model and the experimental procedure. The outcomes of this study can be valuable for the design and analysis of lightweight sandwich plates with porous cores and composite face sheets.

Predicting the relative shear storage modulus of highly filled bidisperse particulate suspensions by machine learning and constrained symbolic regression

2026-09-25T09:57:23+00:00

Highly filled suspensions of rigid spheres in a yield stress fluid have a relative shear storage modulus that is
governed not only by the solid volume fraction but also by the grading of the particles. Classical homogenization schemes contain no maximum packing fraction and therefore cannot reproduce the rapid growth of the modulus that is observed as the solid skeleton approaches its densest state, whereas conventional machine learning delivers accurate predictions in the form of a trained model rather than an explicit expression that a designer can use. This paper compares six machine learning models and two symbolic regression variants on a published set of 70 data points covering seven mixtures of spherical particles in an inverse emulsion, one monodisperse and six bidisperse, with size ratios from 1 to 8.25 and fine fractions from 0 to 0.5; no new measurements were made. The set comprises 63 measurements of G′(φ)/G′(0) and seven normalization points at φ = 0, where the ratio equals unity by definition, and every model is scored on the 63 measurements. The
maximum packing fraction, computed from a compressible packing model, is used as an input feature, and predictive ability is assessed with a leave-one-mixture-out protocol that never places a mixture in both the training and the test set. Over the six bidisperse mixtures the Gaussian process model gives a macro-average mean squared error in logarithmic space of 0.0205 against 0.1164 for the one-parameter power law, and its pooled error rises by a factor of 6.4 when the maximum packing fraction is removed from the feature set. The constrained symbolic regression variant returns a power law with no additional numerical constant fitted in the final exponent and reaches a log-space coefficient of determination of 0.950 over the 63 measurements; under the same leave-one-mixture-out protocol, however, the search procedure that produces it is, for the seed-selection rules tested, less accurate than the one-parameter power law. On 16 measurements of Table 3 of [1] at eight gradings that entered no fit, the expression gives a mean squared error of 0.0308 against 0.0081 for the fixed exponent √2, so the fixed exponent is the better closed form for these suspensions. Constraining the functional form is therefore necessary but not sufficient for generalization to new gradings.

Pineapple leaf conversion to nanocellulose fiber and its impact on the porosity, mechanical strength, and microstructure of cement-based composites

2026-09-25T09:57:23+00:00

The growing demand for sustainable and low-carbon construction materials underscores the importance of developing cementitious composites (CCs) that utilize renewable waste-derived admixtures without sacrificing performance. This study examines the effects of nanocellulose fibers (NF) extracted from pineapple leaf waste– a largely untapped agricultural byproduct–on the mechanical, microstructural, and porosity characteristics of CCs containing Class-F fly ash. CC samples incorporating 0.0–2.0 wt.% NF were evaluated for compressive and flexural strengths, porosity, and microstructural evolution using FTIR, XRD, and SEM analyses. The results revealed that a small addition of 0.5 wt.% NF helped maintain hydration uniformity and retained about 90% of the control compressive strength (74.1 MPa → 66.7 MPa), while higher dosages caused substantial deterioration: compressive and flexural strengths decreased to 10.2 MPa and 2.3 MPa, respectively, corresponding to an approximately 86% reduction in strength and a 253% increase in porosity (10.44% → 36.85%). A strong inverse, nonlinear correlation between porosity and mechanical strength was identified, confirming porosity as the principal factor governing performance. Overall, pineapple leaf–derived NF serves as a promising eco-efficient additive at low concentrations, but excessive dosages lead to agglomeration, hindered hydration, and pore coarsening, emphasizing the need for optimized dispersion and dosage control in bio based fiber–reinforced cement systems. These findings provide insights for the sustainable design of eco efficient CCs.

Journal of Science and Technology in Civil Engineering (JSTCE) - HUCE

Table of Contents

Durability and structural integrity of nuclear power plant concrete: A critical review of materials, ageing, and assessment

A sequential computational framework for recycled-material 3D printing concrete: From pumping flow to buildability limit

Long-term mechanical properties and durability of a biological coating mortar using Rhodobacter capsulatus PN7 immobilized in vermiculite

Predicting compressive creep of SCC based on model B4TW-SCC: Optimization method and experimental

Unified multi-material topology optimization of heterogeneous structures for three-phase multi-physics systems

Experimental and semi-analytical investigation of dynamic behavior of sandwich plates with porous core and CFRP face sheets

Predicting the relative shear storage modulus of highly filled bidisperse particulate suspensions by machine learning and constrained symbolic regression

Pineapple leaf conversion to nanocellulose fiber and its impact on the porosity, mechanical strength, and microstructure of cement-based composites