PSI - Issue 83

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Procedia Structural Integrity 83 (2026) 1–2

The fourth European Conference on the Structural Integrity of Additively Manufactured Materials (ESIAM26) Preface Sabrina Vantadori a,* , Francesco Iacoviello b , Vittorio Di Cocco b a Università di Parma, Dipartimento di Ingegneria e Architettura, Italy b Università di Cassino e del Lazio Meridionale, Dipartimento di Ingegneria Civile e Meccanica, Italy

© 2026 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the ESIAM26 organizers Keywords: Powder Bed Fusion - Laser Based; Al-Mg-Sc-Zr alloy; Low temperature; High temperature; Tensile properties; Portevin–Le Chatelier

Additive manufacturing (AM) enables the production of components with unprecedented geometrical complexity, often impossible or economically unfeasible to obtain through conventional processes. This capability offers major opportunities for designing lightweight, optimized, and highly functional structures. However, for load-bearing and safety-critical applications, their implementation depends on ensuring structural integrity throughout the entire service life. AM also introduces specific challenges related to mechanical reliability, damage tolerance, and in-service performance. Complex geometries, internal features, lattice structures, thin walls, and local cross-section variations may strongly influence stress distributions and create critical sites for damage initiation. In addition, process-induced imperfections, including porosity, lack of fusion, surface roughness, residual stresses, anisotropy, and microstructural heterogeneity, can significantly affect fracture resistance, fatigue life, and crack propagation behaviour. Therefore, proper characterisation, modelling, and interpretation of geometry-related effects and manufacturing-induced defects are essential for reliable structural integrity assessment and for qualifying AM in critical engineering applications. Within this framework, the aim of ESIAM26, the Fourth European Conference on the Structural Integrity of Additively Manufactured Materials, held in Vicenza, Italy, and online on February 18-20, 2026, was to foster a dedicated community working on this strategic topic.

* Corresponding author. . E-mail address: sabrina.vantadori@unipr.it

2452-3216 © 2026 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the ESIAM26 organizers 10.1016/j.prostr.2026.07.001

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The Conference provided a platform for exchanging experience, sharing data and information, and promoting discussion among researchers, engineers, and industrial stakeholders. ESIAM26 gathered more than 120 participants from over 30 countries, confirming the growing international interest in the structural integrity of additively manufactured components. This special issue of Procedia Structural Integrity collects 32 papers based on the contributions presented during ESIAM26. Consistently with the scope of the conference, the volume addresses key aspects related to the structural integrity of additively manufactured materials and components. We hope that these contributions will stimulate further scientific discussion, promote international collaboration, and support the development of robust methodologies for the design, assessment, and certification of additively manufactured components, ultimately contributing to safer, more reliable, and more sustainable engineering structures.

Figure 1: Session.

Figure 2: Best Symposium Award Ceremony.

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Procedia Structural Integrity 83 (2026) 63–71

The fourth European Conference on the Structural Integrity of Additively Manufactured Materials (ESIAM26) 3D Printable Self-Sensing Cementitious Composites: Evaluating

Promise versus Practicality Samia M. Mohamed a , Asad Hanif a,b, *

a Civil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia b Interdisciplinary Research Center for Construction and Building Materials, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia

© 2026 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the ESIAM26 organizers Keywords: Self-sensing; 3D printing; Structural health monitoring; Mechanical properties; Cementitious composites Abstract Additive manufacturing of cementitious composites has revolutionized construction industry, enhancing sustainability, improving productivity, and substantially reducing labour and material waste. In this review, recent advancements in self-sensing 3D-printed cementitious composites (3DP-CCs) are studied, establishing correlations between sensing mechanisms, material selection, printability requirements, and measurement practice. A structured comparative analysis is presented across matrix constituents, conductive filler and dosage, essential printing parameters, mechanical properties, and electrical response. Reported performance is typically assessed through baseline electrical properties, i.e. resistivity or conductivity, and evaluated using primary sensing metrics, such as fractional change in resistance and gauge factor. In addition, key challenges alongside future research directions aimed at optimizing material design, processing strategies, measurement practices, and reporting standards for field implementation are critically examined in this review. Overall, this review highlights the potential of self-sensing 3DP-CCs to deliver cost-efficient, low-impact structural components that address contemporary construction demands while mitigating environmental burdens. Realizing this potential, however, requires converting laboratory-scale performance into standardized, durable, and scalable solutions for practical adoption.

* Corresponding author. Tel.: +966-13-8603630 E-mail address: asad.hanif@kfupm.edu.sa; ahanif@connect.ust.hk

2452-3216 © 2026 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the ESIAM26 organizers 10.1016/j.prostr.2026.07.008

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1. Introduction Technological innovations in the construction sector have driven the emergence and adoption of advanced construction methodologies that enhance structural quality and ensure safety performance in the built environment. Additive manufacturing of 3D printing cementitious composites (3DP-CCs) has gained importance in the last two decades, offering substantial gains in productivity, reduction in material consumption and waste, diminished dependence on formwork and manual labour, and expanded geometric versatility for structural components (Soares et al., 2023; Sousa et al., 2024). Extrusion-based 3D concrete printing (3DCP) is advancing as a digital construction method that deposits cementitious material layer-by-layer from a nozzle, enabling automated fabrication of complex geometries and reducing dependence on conventional formwork (Mohan et al., 2021). Achieving reliable 3DCP performance depends strongly on fresh-state engineering, pumpability, extrudability, buildability, and open time, because printable mixes must flow through the system yet quickly gain green strength to sustain subsequent layers (Rahman et al., 2024). Recent systematic and state-of-the-art reviews emphasize that controlling rheology and process parameters is essential for consistent structural quality and for mitigating print-induced defects such as interlayer weakness and anisotropy (Mohan et al., 2021; Rahman et al., 2024). In parallel, self-sensing cementitious composites have emerged as “smart” materials for structural health monitoring, where stress/strain-related changes are detected through electrical signals, most commonly via piezoresistivity enabled by conductive fillers, e.g., carbon black, CNTs, graphene, and carbon fibers. Their sensing response is frequently quantified using fractional change in resistance (FCR) and gauge factor (GF) concepts, which link electrical response to mechanical strain and allow comparison of sensitivity across mixes and loading regimes. However, reported sensing performance is highly dependent on dispersion quality, percolation, moisture/ionic effects, and measurement protocols, which have motivated ongoing efforts to improve repeatability and interpretation of piezoresistive signals (Dang et al., 2021; Li et al., 2025). Despite rapid growth, the intersection of 3D printability and self-sensing functionality introduces coupled constraints that are not fully resolved by treating “printability” and “sensing” as separate topics: printing can align fibers, create interlayer interfaces, and induce anisotropy, which directly affects conductive network formation and thus sensing stability (Sousa et al., 2024; Wang et al., 2022). Recent studies demonstrate the feasibility of 3D-printed self-sensing structural elements and embedded sensing features, but also highlight variability tied to geometry, electrode design, and print-induced heterogeneity, emphasizing the need for clearer design rules and benchmarking (Rahman et al., 2024; Ramachandran et al., 2022). The novelty and value of this review is to develop the promise versus-practicality trade-offs across: printable mix design and rheology windows, conductive filler selection and dispersion strategies, print-process effects on interlayer conductivity and anisotropy, and sensing metrics/testing protocols (FCR and GF), to identify what is genuinely functional for smart infrastructure rather than only “lab demonstrable.” 2. Self-sensing mechanism and materials development 2.1. Self-sensing mechanism Self-sensing cementitious composites function mainly through the piezoresistive effect, meaning that the electrical resistance or resistivity of the composite changes when mechanical stress, strain, or damage alters its internal conductive network. This resistance-based approach has been the dominant self-sensing strategy in cement-based materials since the field emerged in the early 1990s, and it remains the most widely studied route for structural health monitoring applications. Because plain cement paste is primarily an ionic conductor with high moisture sensitivity, conductive admixtures are typically added to improve electrical conductivity, signal stability, and sensing sensitivity. The most common fillers reported in the literature are short carbon fibers (CFs), carbon nanotubes (CNTs), carbon nanofibers (CNFs), carbon black (CB), graphite/graphene-based fillers, and hybrid combinations of these materials (Bekzhanova et al., 2021; Chung, 2020; Lee et al., 2017). At the microscale, the sensing response is governed by changes in contact conduction, tunneling conduction, and crack-induced disruption of conductive pathways inside the cementitious matrix. When the conductive filler

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content approaches the percolation threshold, a connected or nearly connected network forms, so even small changes in particle spacing or fiber contact caused by loading can produce measurable resistance changes. Due to this reason, filler dosage and dispersion quality are critical: poor dispersion causes agglomeration and unstable signals, whereas well-dispersed CNTs or CNFs can reduce resistivity and improve piezoresistive sensitivity. Previous studies have shown that effective ultrasonic dispersion with surfactants is particularly important for CNT-reinforced cement systems, while hybrid filler systems can improve sensing performance by combining conductive pathways across different length scales (Konsta-Gdoutos et al., 2010; Konsta-Gdoutos and Aza, 2014; Lee et al., 2017b). When mechanical loading is applied, the distance between conductive particles changes, causing either formation or disruption of conductive pathways, which results in measurable resistance variation. The sensing response is commonly expressed using fractional change in resistance (FCR), usually written as Δ / ଴ , and gauge factor (GF), which relates normalized resistance change to mechanical strain, as shown in Fig. 1. These metrics are widely used to compare sensitivity across different self-sensing cementitious systems, but their values depend strongly on filler type, percolation state, loading mode, curing age, moisture condition, electrode configuration, and test method. For example, a study used hybrid CF and multi-walled carbon nanotube (MWCNT) reported that composites containing 0.1 vol.% CF + 0.5 vol.% MWCNTs achieved sensing performance comparable to 1.0 vol.% MWCNTs (Lee et al., 2017), with a reported GF of 160.3, showing that hybrid system can improve efficiency while reducing nanotube demand. In more ductile cementitious systems such as Engineered cementitious commposite(ECC), self sensing behavior has also been extended beyond compression toward tensile strain and crack monitoring, which is important because conventional brittle cement-based materials are often limited in tensile sensing studies (Elseady et al., 2023; Huang et al., 2018).

Fig. 1. (a) 3D printing-induced anisotropy and sensing pathways, and (b) Typical FCR-strain response (cyclic loading)

2.2. Materials Development for Self-sensing Cementitious Composites The development of self-sensing cementitious composites (SS-CCs) is fundamentally a multi-objective mix design challenge, the conductive phase must be high enough to create a stable percolated network, but not so high that

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dispersion deteriorates, agglomeration increases, or the matrix loses mechanical integrity. Across studies, the most common conductive systems are CF, MWCNTs, CNFs, CB, graphite/graphene-related fillers, and hybrid combinations of micro- and nano-carbon fillers. These fillers are not functionally similar, CF often contributes crack bridging and toughness, CNT/CNF systems typically reduce percolation threshold and improve sensitivity, while particulate fillers such as CB or graphite can strongly reduce resistivity but may require careful dosage control to avoid excessive strength loss or unstable dispersion (Dogra et al., 2025; Donnini et al., 2018a; Taheri et al., 2022). A consistent observation reported across the literature is that the optimum sensing performance of cementitious composites is typically achieved near the percolation threshold of the conductive network, rather than at the maximum filler dosage. At this condition, the conductive phase forms a sufficiently connected network to enable stable electrical conduction, while avoiding excessive agglomeration or disruption of the cementitious matrix (Dinesh et al., 2023; Lee et al., 2017b; Zhao et al., 2024). For example, Zhao et al., demonstrated that in graphite-modified cement composites, the incorporation of 0.6 vol.% CF into a matrix containing 20 wt.% graphite powder resulted in a peak compressive strength of 43.01 MPa, a maximum resistivity change of − 76.8%, and a strain sensitivity factor of 1300 under cyclic compression (Zhao et al., 2024). In another graphite-based self-sensing cement composite, the highest reported stress sensitivity (1.35) was obtained at 5% graphite powder, whereas increasing the graphite content to 25% reduced electrical resistivity by 43.9% but resulted in a reduction in mechanical strength (Dinesh et al., 2023). Collectively, these findings indicate that the performance of self-sensing cementitious composites is governed by a delicate balance between conductive network formation and matrix integrity, highlighting that maximizing filler content does not necessarily lead to optimal sensing behavior. Beyond filler type and dosage, materials processing and dispersion strategies play a critical role in determining the stability and reliability of the sensing response. Techniques such as ultrasonic dispersion, surfactant assisted mixing, and hybrid filler systems are commonly employed to improve the homogeneity of the conductive network and minimize particle agglomeration. For instance, a CNT-modified UHPFRC system reported that incorporating 0.3 wt.% CNT or higher enabled crack-related self-sensing under compressive loading, while the same dosage allowed the pre-peak flexural response to be effectively captured through resistivity variation. In a more recent study focusing on embeddable sensing nodes, mixtures containing 0.2% reduced graphene oxide (rGO) and 2.5% carbon black (CB) exhibited the most stable piezoresistive behavior, with a reported fractional change in resistance (FCR) approaching 400% during reinforced concrete beam monitoring. Furthermore, red-mud-based self-sensing mortars incorporating carbon black nanoparticles showed that increasing red mud replacement from 25% to 100% led to higher viscosity and yield stress, accompanied by a 60% reduction in the consistency index, while simultaneously improving compressive strength by up to 80% and increasing the gauge factor by approximately 23%. These findings demonstrate that the development of self-sensing cementitious materials should not be considered solely from the perspective of conductive filler content, but must also account for dispersion methodology, matrix composition, and fresh-state rheological characteristics, which collectively influence the formation and stability of the conductive network (Dogra et al., 2025; Oliveira et al., 2025; You et al., 2017). Table 1 summarizes representative studies on SS-CCs, including both conventionally cast materials and extrusion-based 3D-printed systems. The comparison highlights the matrix composition, type and dosage of conductive fillers, fresh-state or printing characteristics, mechanical properties, and sensing performance reported in the literature. Carbon-based conductive additives, such as carbon fibers (CF), CNTs, graphene-derived materials (GO and rGO), CB, and graphite, are the most commonly adopted fillers for establishing conductive networks within cementitious matrices. As shown in the table, the sensing capability of these materials is strongly influenced by filler type, dosage, and dispersion method, as well as by matrix composition and processing route. In particular, several studies demonstrate that hybrid conductive systems and optimized filler contents can significantly enhance piezoresistive sensitivity while maintaining acceptable mechanical performance. In addition, extrusion-based 3D printing introduces further complexities related to rheology, interlayer bonding, and print-induced anisotropy, which may affect both structural behavior and sensing reliability. Overall, the compiled studies illustrate the progress achieved in developing multifunctional cementitious materials capable of structural load bearing and self-sensing, while also highlighting the limited number of investigations that simultaneously address mix design, printability, mechanical performance, and sensing behavior within the same experimental framework.

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Table 1. Summary of representative studies on self-sensing cementitious composites and 3D-printed self-sensing cementitious systems

Fresh/printing properties

Mechanical properties

Reference Matrix composition

Conductive filler CF = 0.7 wt% of binder + ACP = 0.25 wt% of binder Graphite (10 wt%) + MCMF (0.25 wt%) + CCMF (0.125 wt% CF (0.7 wt%) + ACP (0.25 wt%) Graphite 3 wt% + MCMF 0.25 wt% + CCMF 0.25 wt% CPC = 150 kg/m³ + PE fiber 1.5 vol% CF (0.7 wt%) + ACP (0.25 wt%) Graphite (10 wt%) + MCMF (0.25 wt%) + CCMF (0.167 wt%)

Sensing results

The best piezoresistive response reported when loading was perpendicular to the print direction. Gauge factor GF = 622, strain resolution 167 µ ε , accuracy 19.2 µ ε  Resistivity 274 Ω ·cm ↓  Sensing degraded after shell cracking

Cement (31.8%), slag (38.1%), SF (3.5%), silica sand (26.6%) Cement (100), w/c ≈ 0.36–0.58 (31.8%), slag (38.1%), SF (3.5%), silica sand (26.6%) cement (28.5), sand (40.8), SF (9.3), quartz (8.4), water (4.4) Cement (48.9), SF (15.0), FA (11.3), FAC (18.0), sand (7.5) (31.8%), slag (38.1%), SF (3.5%), silica sand (26.6%) Cement (100), w/c = 0.583 matrix (cement + calcined clay + limestone) Cement UHPC: Cement LC³-based

Wang et al., 2022

Printable at w/b 0.325; good extrudability

 f`c = 74.9 MPa  ft = 16.4 MPa

Young’s modulus 798 MPa ↑ 42.2% vs plain

Liu et al., 2024 Atkinson and Aslani, 2023 Liu et al., 2024

Extrusion printing

Extrusion

printed

f`c = 56.02 MPa

column shells

 Cast GF 540  printed GF 410 ↓  resolution 99 µ ε

 f`c ↑ 9.8%  Elastic modulus ↑ 19.2%  f`c = 82.6MPa  ensile strain capacity ↓ ~18%

_

Nozzle 30 mm, speed 40 mm/s

Hu et al., 2026

GF 1427–1828

Wang and Aslani, 2023 Liu et al., 2025 Nandurkar et al., 2025 Donnini et al., 2018

Printed

sensor

Printed sensor GF 318, bulk GF 527 ↑

_

embedded in beam

_

_

GF 669, SNR 10.8 dB ↑

Conductivity ↑ 25–35%, interlayer conductivity ↑ 40–50%, defect detection >95%  Resistivity reduced to <150 Ω ·cm. Optimum compromise reported at ~3 wt.% CF Mortars with 50–100% RM showed reversible sensing.  GF improved by 23% Resistivity dropped from 260.4 × 10³ Ω ·cm (without CNT) to 393 Ω ·cm with 0.5% CNT and to 323 Ω ·cm after sonication. 0.3 wt.% CNT enabled crack related self-sensing Composites with 25% GP showed 43.90% lower resistivity, max stress sensitivity = 1.35  FCR = 22.7% at 0.25% PP + 0.7% CF.  Resistivity at 1.5% PP was 53.2% higher than 0.25% PP when CF = 0.5%.

Hybrid

nano

_

_

carbon fillers

 ft ↑ with CF content. f`c showed no improvement

Cement-based mortar

CF: 2, 3, 4 wt % of cement

Workability ↓

↑ RM ↑ viscosity and yield stress. Consistency index decreased by 60%. caused

Oliveira et al., 2025

Cement + RM

CB

 f`c ↑ 80%

SFs (0.1 vol%) +MWCNT (0.5 vol%)

You et al., 2017

UHPFRC / UHPFRC with CNT

_

f`c ↑ with SFs.

Dinesh et al., 2023

Cement-based composite

f`c ↑ with SF by 16.5%  Best f`c at 0.5% PP + 0.9% CF.  f`c at 0.9% CF was 14.4– 17.6%.  ft at 0.5% CF improved by 31.2%.

5% GP

_

Ma et al., 2024

ECC with glass sand replacing silica sand

PP fibers + CF

_

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rGO, CB, GO; best at 0.2% rGO and 2.5% CB

Dogra et al., 2025

Cement-based composite

_

_

Max FCR up to 400%

SF: Silica fume, ACP: Activated carbon, f`c: Compressive strength, ft: flexural strength, MCMF: Milled carbon microfiber, CCMF: Chopped carbon microfiber, FA: Fly ash, FAC: Fly ash cenosphere, LC³: Limestone Calcined Clay Cement, RM: Red mud, UHPFRC: Ultra-high performance fiber reinforced concrete, GP: Graphite powder, ECC: Engineered cementitious composites, rGO: reduced carbon oxide, GO: Carbon oxide. 3. Limitations, Challenges, and Future Directions Despite significant advances in the development of conductive cementitious composites and extrusion-based additive manufacturing technologies, the integration of 3D printing with self-sensing functionality in cementitious materials remains at an early stage of development. The studies summarized in Table 1 demonstrate promising sensing capabilities and mechanical performance; however, several limitations continue to hinder the practical implementation of these materials in structural applications. 3.1. Limited Integration of Printability, Mechanical Performance, and Sensing Behavior One of the primary challenges in developing 3D-printed self-sensing cementitious composites is the simultaneous optimization of printability, mechanical performance, and sensing functionality . Many existing studies tend to focus on one aspect while treating the others as secondary considerations. For example, Wang et al. demonstrated that hybrid carbon-based conductive systems consisting of carbon fibers (CF) and activated carbon powder (ACP) can significantly enhance both compressive strength and piezoresistive sensing behavior in printable cementitious composites, achieving compressive strengths up to 74.9 MPa while maintaining a stable sensing response. However, the mixture design was primarily optimized for sensing performance rather than large-scale structural printability (Wang et al., 2022). Similarly, Liu et al. developed hybrid graphite–carbon microfiber self-sensing composites and reported a GF of 622 with high strain resolution, highlighting the potential of hybrid conductive systems for high-sensitivity sensing (Liu et al., 2024b). Nevertheless, the investigation was conducted at the small-specimen level, and issues related to structural printing, durability, and large-scale fabrication were not addressed. In another study on printable ultra-high performance concrete (UHPC), Liu et al. showed that the addition of graphite and carbon microfibers could increase compressive strength and elastic modulus by 9.8 % and 19.2 %, respectively, while enabling reliable sensing behavior, although the gauge factor of printed specimens was lower than that of cast specimens (Liu et al., 2024a) These findings indicate that a comprehensive design strategy that simultaneously considers rheology, mechanical properties, and sensing behavior is still missing in the current literature. 3.2. Influence of Print-Induced Anisotropy and Interlayer Interfaces Extrusion-based additive manufacturing inherently produces anisotropic microstructures, as the material is deposited layer by layer along a defined printing path. This anisotropy can influence both the mechanical behavior and the electrical conductivity of printed cementitious materials. For example, Wang et al. reported that the strongest piezoresistive response occurred when the loading direction was perpendicular to the printing direction, suggesting that the orientation of conductive fillers and filaments during extrusion strongly affects the sensing response (Choi et al., 2022). Similarly, structural-scale studies have shown that print orientation and interlayer bonding significantly influence both mechanical strength and electrical resistivity. For instance, the mechanical and piezoresistive behavior of 3D-printed self-sensing slab elements was found to vary depending on the printing direction, with compressive strengths reaching 56.02 MPa and resistivity values around 355 Ω ·cm, while the strongest sensing response occurred in the direction perpendicular to the printing path (Sun et al., 2025). Another structural investigation demonstrated that 3D-printed cement-based sensing elements embedded in reinforced concrete beams can successfully capture strain and crack formation during loading, although the sensing performance of printed sensors was lower than that of bulk

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self-sensing composites (Wang and Aslani, 2023). These results highlight the critical role of interlayer bonding quality and conductive network continuity in determining the sensing reliability of printed materials. 3.3. Dispersion and Percolation Challenges of Conductive Fillers The sensing performance of cementitious composites is strongly dependent on the formation of a continuous conductive network within the matrix. In most self-sensing systems, this network is formed when the conductive filler content approaches the percolation threshold , where electrical conductivity increases dramatically due to the formation of interconnected conductive pathways. However, maintaining uniform dispersion of conductive fillers within the cement matrix remains a significant challenge. Several studies have shown that carbon-based fillers such as carbon fibers, carbon nanotubes, and graphene derivatives can significantly reduce electrical resistivity and improve sensing sensitivity. For example, You et al. reported that incorporating multi-walled carbon nanotubes into UHPFRC systems reduced electrical resistivity from 2.6 × 10 ⁵Ω ·cm to approximately 393 Ω ·cm , while also enabling crack-related self sensing behavior (You et al., 2017). Similarly, Dogra et al. demonstrated that hybrid graphene-based systems containing rGO and CB could achieve fractional changes in resistance of up to 400 %, indicating extremely high sensing sensitivity (Dogra et al., 2025). However, nanoscale fillers frequently agglomerate and have poor dispersion in the highly alkaline cement matrix, resulting in unstable electrical signals and impaired mechanical performance. As a result, enhancing dispersion techniques and optimizing filler dosage remain important research targets. 3.4. Limited Structural-Scale Validation Although numerous studies have demonstrated promising sensing performance at the material level , structural-scale validation remains relatively limited. Most investigations have been conducted on small laboratory specimens such as cubes, prisms, or small printed elements. Only a few studies have explored the integration of self-sensing cementitious composites into full-scale structural components. For example, Atkinson and Aslani investigated 3D-printed self sensing column shells and reported that while printed specimens exhibited compressive strengths of approximately 56 MPa , sensing reliability could deteriorate once structural cracking occurred (Atkinson and Aslani, 2023). Similarly, embedded self-sensing nodes fabricated by 3D printing have been proposed as a strategy for monitoring structural behavior during loading (Liu et al., 2025). These studies demonstrate the feasibility of integrating sensing functionality into structural components, but they also highlight the need for further research on long-term durability, signal stability, and environmental effects under real service conditions. 3.5. Future Research Directions Future research should focus on developing integrated material design approaches that simultaneously address the competing requirements of printability, mechanical performance, and sensing functionality. Hybrid conductive systems combining micro-scale fibers and nano-scale fillers appear particularly promising, as they can enhance electrical conductivity while maintaining acceptable mechanical properties. Another promising direction is the use of data-driven material design and machine-learning techniques to optimize conductive networks in printable cementitious composites. For example, Nandurkar et al. proposed a multi-scale deep learning framework for optimizing 3D-printed self-sensing cementitious composites containing hybrid nano-carbon fillers, reporting improvements of 25–35 % in electrical conductivity and 40–50 % in interlayer conductivity (Nandurkar et al., 2025). Finally, future studies should investigate the long-term durability and environmental stability of these materials under realistic service conditions, including cyclic loading, moisture variations, and temperature fluctuations. Addressing these challenges will be essential for enabling the practical deployment of smart, self-monitoring cementitious infrastructure fabricated through additive manufacturing .

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4. Conclusions This review examined recent advances in 3D-printed self-sensing cementitious composites, focusing on material design, conductive filler systems, and the integration of sensing functionality with extrusion-based additive manufacturing. Carbon-based fillers such as carbon fibers, carbon nanotubes, graphite, graphene derivatives, and carbon black have been widely used to establish conductive networks within cementitious matrices, enabling strain dependent electrical responses. Hybrid filler systems have shown particularly promising performance, achieving high sensing sensitivity and reduced electrical resistivity. Similarly, CNT-modified cementitious composites demonstrated significant reductions in electrical resistivity and improved crack-related sensing capability. Despite these advances, integrating self-sensing functionality with extrusion-based 3D printing remains challenging due to competing requirements between printability, mechanical performance, and sensing behavior. Conductive fillers can alter rheological properties, affecting extrusion stability and layer deposition, while print induced anisotropy may influence the orientation of conductive networks and the resulting sensing response. Furthermore, structural-scale validation of 3D-printed self-sensing cementitious materials remains limited. Although studies have demonstrated the feasibility of embedding 3D-printed sensors in reinforced concrete beams and printing self-sensing slab elements, long-term durability, signal stability, and real-world performance require further investigation. Future research should focus on integrated mix design strategies, improved conductive filler dispersion, and structural-scale validation of printable self-sensing composites. Emerging approaches, such as machine-learning assisted material optimization, may also accelerate the development of multifunctional smart cementitious materials for structural health monitoring References: Atkinson, C.D., Aslani, F., 2023. Performance of 3D printed columns using self-sensing cementitious composites. Constr. Build. Mater. 375, 130961. https://doi.org/10.1016/j.conbuildmat.2023.130961 Bekzhanova, Z., Memon, S.A., Kim, J.R., 2021. Self-Sensing Cementitious Composites: Review and Perspective. Nanomaterials 11, 2355. https://doi.org/10.3390/nano11092355 Choi, E., Ostadrahimi, A., Kim, W.J., 2022. Enhancement of compressive strength and strain ductility of SMA fiber reinforced concrete considering fiber’s aspect ratios. Constr. Build. Mater. 345, 128346. https://doi.org/10.1016/j.conbuildmat.2022.128346 Chung, D.D.L., 2020. A critical review of piezoresistivity and its application in electrical-resistance-based strain sensing. J. Mater. 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Procedia Structural Integrity 83 (2026) 162–170

© 2026 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the ESIAM26 organizers Keywords: Predictive maintenance, crack detection, Euler–Bernoulli beam, Functionally graded material, Particle Swarm Optimization ; Abstract This paper conducts a comprehensive investigation into the utilization of the Particle Swarm Optimization (PSO) algorithm for crack detection in beam structures. The primary aim is to precisely forecast both the size and location of an open-edge crack in functionally graded beams. The chosen modeling approach involves a rotating spring, with its stiffness determined by the crack's size. The PSO algorithm is employed as an optimization technique to address this problem. The objective function is formulated as the weighted sum of squared errors between measured and calculated natural frequencies. The PSO algorithm systematically explores the solution space to identify optimal values for crack size and location, minimizing the defined objective function. The obtained results illustrate the efficacy of the PSO algorithm in accurately predicting crack size and location. This methodology provides a viable alternative for addressing crack detection challenges, opening avenues for potential applications in predictive maintenance of beam structures. In conclusion, the application of the PSO algorithm underscores its relevance and effectiveness in resolving crack detection issues, offering a promising solution for safeguarding structural integrity and averting future failures. The fourth European Conference on the Structural Integrity of Additively Manufactured Materials (ESIAM26) Advancing predictive maintenance: An optimization approach for detecting cracks in Functional Gradient Beam Structures Amal Lahrizi a *, Ayad Ghassane a , Abdelhamid Zaki b a Laboratoire Mécanique, Matériaux et Thermique (LMMT), École Nationale Supérieure des Mines de Rabat (ENSMR), Mohammed V University in Rabat, B.P. 753, Agdal, Rabat, Morocco b Laboratoire de recherche Artificial Intelligence & Complex Systems Engineering (AICSE), ENSAM-Casablanca, Université Hassan II, B.P. 20670, Casablanca, Morocco

* Corresponding author. Tel.: +212 7 66 66 02 68. E-mail address: a.lahrizi@enim.ac.ma

2452-3216 © 2026 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the ESIAM26 organizers 10.1016/j.prostr.2026.07.019

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1. Introduction In modern industry, monitoring the condition of structures and detecting damage at an early stage are crucial to ensuring the safety and performance of systems. Structures in service are subject to varied and cyclical stresses, which can lead to structural defects, such as cracks, that can cause catastrophic failures. The ability to predict and prevent these failures is essential to maintaining structural integrity. Non-destructive inspection methods, particularly those based on vibrations, have emerged as effective and economical approaches for monitoring changes in the properties of structures. Cracks, by altering the dynamic stiffness of components, have an impact on vibration characteristics, particularly natural frequencies and eigenmodes. This leads to crack detection being approached as an inverse problem, where modal data from testing is used to identify crack characteristics. Among the key structural elements, beams play an essential role in the distribution of loads. However, cracks are proving to be a worrying threat to their integrity. While traditional non-destructive testing methods have their limitations for complex structures, vibration based methods, which take into account variations in natural frequencies, are gaining in relevance. These frequency variations are more accessible to measurement and offer crucial information for detecting and predicting the location and extent of cracks [1]. Over the past few decades, research efforts have been prominently directed towards utilizing vibrations and variations in natural frequencies for the identification of structural damage. Numerous research papers have played a pivotal role in advancing this field. In the study by Outassafte et al. [2], the focus was on detecting cracks in circular arches through an examination of natural frequency variations, employing the Firefly hybrid algorithm. Y. El Khouddar et al. [3] delved into the influence of hygrothermal effects on the free and forced vibrations of functional gradient piezoelectric beams, presenting potential applications in challenging environments. A parallel approach was pursued by Outassafte et al. in their investigation [4], where the authors analyzed the linear and geometrically non linear plane vibrations of a damaged circular arc, thereby laying the foundational groundwork for a promising methodology in predicting structural damage through natural frequency variations. Recent research has expanded the application of vibration-based damage detection techniques to functionally graded beams (FGMs). Within the context of FGM structures, Banerjee et al. [5] introduced a novel approach to crack modeling and detection. They utilized frequency contour analysis and a response surface model with a genetic algorithm (GA) to successfully detect cracks in Timoshenko fiberglass beams subjected to transverse vibrations. Another noteworthy contribution to the condition monitoring of FGM structures comes from Lahrizi et al. [6,7]. Their work applied an optimization algorithm based on the transit search technique to predict and identify cracks in functional gradient beam structures, showcasing a highly efficient detection method. While not directly focused on crack detection, the paper [8] imparts valuable insights into the forced vibrational behavior of complex beams, contributing to a more profound understanding of vibrational responses in functional gradient structures. Finally, in [9], the authors conducted a non-linear analysis of forced vibrations of piezoelectric functional gradient beams in a thermal environment, concentrating on non-linear responses and shedding light on the vibrational behavior under various environmental conditions, which could have implications for crack detection strategies. Collectively, these research papers provide a comprehensive foundation for the study of modeling and detecting cracks in piezoelectric functional gradient beams subjected to transverse vibrations. The present paper places a specific emphasis on the innovative application of the bee algorithm to address the intricate challenge of crack detection in beams. The bee algorithm emerges as a promising solution, capitalizing on natural frequency changes for precise crack prediction. This study signifies a substantial contribution to the enhancement of predictive maintenance practices and the overall durability of structures exposed to diverse stresses.

Nomenclature 11 A

Tensile stiffness coefficient

11 B

Flexural-tensile coupling stiffness coefficient

( , ) E z T Young's modulus z M Bending moment x N Force axiale n

Volume fraction exponent

Amal Lahrizi et al. / Procedia Structural Integrity 83 (2026) 162–170

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( , ) P z T Effective ownership of the material ( , ), ( , ) u x t w x t   * * w x Shape of the nth linear mode of the beam * x Non-dimensional coordinate , , x y z Structure axis system

Movements in the x and z directions, respectively

2. Modal Analysis of Cracked FGM Beams: Modelling and Vibratory Behaviour Comprehending the vibrational behavior of structures with cracks holds immense significance in the proactive detection of damage. When conceptualizing a structure as a linear system, the changes in its characteristics, characterized by the stiffness and mass matrices, have a direct impact on alterations in its frequencies and eigenvectors [10]. In the scope of our investigation, our attention is directed towards a bi-encased Functionally Graded Material (FGM) beam characterized by length L , height h , and width b , featuring an open transverse crack with a depth denoted as dc (refer to Fig. 1).

Fig. 1. Representative model of an FGM beam with a crack.

In order to characterize the vibrational behavior of the cracked beam, we partition it into two uniform segments connected by a torsion spring positioned at the crack location (refer to Fig. 1). The stiffness of this torsion spring, influenced by both the depth of the crack and the physical and geometric attributes of the beam, encapsulates the impact of the crack on the natural frequencies. The coefficient of the torsion spring, represented as K  , is computed using the equation [11]:       2 1 / 6 1 eff c EI K h f d h     (1) where   eff EI ,  and c d represent the effective bending stiffness, Poisson's ratio, and crack depth of the FGM beam, respectively. The geometric parameter   / c f d h is determined through the following calculation [12]:

2

3

4

5

6

c d       h

c d       h

c d       h

c d       h

c d       h





/ f d h

1.8224

3.95

16.375

37.226

76.81











c

(2)

7

8

9

10

c d       h

c d       h

c d       h

c d       h

126.9

172

143

66.56









This model allows us to explore the subtle changes in the vibratory properties of the beam as a function of crack depth. Indeed, the effect of the crack is localised, which justifies the representation in distinct segments linked by the rotating spring. By combining the concepts of modal analysis and dynamic behavior, we are able to decipher how the presence of cracks affects the vibratory properties of the beam.