Researchers have developed an innovative approach to predict the working status of high-formwork support systems (HFSS) by combining finite element model (FEM) simulations with deep learning and large language models (LLMs). Published in *Smart Construction*, this study addresses the challenges of structural health monitoring (SHM) by leveraging a genetic algorithm (GA)-optimized FEM to generate training data for a convolutional neural network (CNN) classifier. The framework also integrates a Retrieval-Augmented Generation (RAG) model with a knowledge graph (KG) to automatically generate reasonable SHM reports for HFSS, demonstrating superior performance over common methods.

image: The proposed framework integrates FEM simulations, CNN classification, and an RAG model for automated SHM reporting, achieving high accuracy in structural status prediction and report generation for HFSS.

Credit: Linlin Zhao/Beijing University of Technology, Jasper Mbachu/Bond University, Siyu Liu/Beijing University of Technology, Rongtian Zhang/Beijing University of Technology

High-formwork support systems (HFSS) are critical in construction but prone to collapses due to inadequate monitoring. Common methods rely on expensive and complex experiments, limiting their practicality. This study proposes a data-driven solution using FEM simulations and deep learning to predict HFSS working statuses—normal, local instability, and fully unstable—with high accuracy.

The research begins with developing and optimizing an FEM of an HFSS using a genetic algorithm (GA) to minimize discrepancies between simulated and experimental data. The optimized FEM generates datasets for training a CNN classifier, which achieves an impressive accuracy in predicting structural statuses. Experimental validation on a full-scale HFSS confirms the CNN's superiority over support vector machines (SVM), with the CNN outperforming SVM in classification tasks.

To streamline SHM reporting, the study introduces a Retrieval-Augmented Generation (RAG) model, combining GPT-4 with a domain-specific knowledge graph (KG). The RAG model generates detailed SHM reports, evaluated using metrics like BLEU, ROUGE, and cosine similarity, demonstrating its effectiveness in producing accurate and contextually relevant reports compared to standalone GPT-4.

Key contributions of the study include:

1. A GA-optimized FEM for generating reliable training data under varied HFSS conditions.

2. A CNN classifier with high accuracy in predicting structural statuses.

3. An RAG model that automates SHM report generation, reducing reliance on subjective expertise.

The framework's potential applications extend to complex structures, with future work focusing on integrating multimodal models and edge computing for broader deployment.

This paper, "Working Status Prediction for a High-Formwork Support System Using Finite Element Model-Informed Deep Learning and GPT-Aided Method," was published in Smart Construction.

Zhao L, Mbachu J, Liu S, Zhang R. Working Status Prediction for a High-Formwork Support System Using Finite Element Model-Informed Deep Learning and GPT-Aided Method. Smart Constr. 2025(2):0015, https://doi.org/10.55092/sc20250015.

Source from [https://www.eurekalert.org/news-releases/1090156].

Researchers from Nanyang Technological University have developed a novel framework that integrates worker self-reportsaZ with objective expert evaluations to address the challenge of validating human reliability models. They also proposed a new hybrid inference model combining Self-Organizing Maps (SOM) and Bayesian Networks (BN) to more accurately predict worker performance and identify potential failures. Published in Smart Construction, this research transcends the limitations of traditional expert-driven methods, offering a validated, data-driven approach to enhance workplace safety and operational efficiency in the high-risk construction industry.

image: The proposed framework for human performance reliability evaluation consists of three phases. First, data is obtained via subjective worker self-assessments and objective expert evaluations. Second, the data is preprocessed using rank standardization and fuzzy set theory to derive an adjusted reliability score for Common Performance Conditions (CPCs) . Finally, in the prediction phase, a SOM-BN inference engine uses this score to predict the workers' control modes.

Credit: Yamo Cao/Nanyang Technological University, Zeren Jin/Nanyang Technological University, Yuguang Fu/Nanyang Technological University

The construction industry is recognized as one of the most hazardous sectors, with human error being a primary cause of most workplace accidents. To assess and mitigate these risks, Human Reliability Analysis (HRA) methods are employed, with second-generation techniques like the Cognitive Reliability and Error Analysis Method (CREAM) being prominent for their focus on cognitive and contextual factors. However, traditional CREAM models suffer from significant limitations: they rely heavily on subjective expert judgment, lack independent ground-truth for validation, and often oversimplify the complex, non-linear interactions between performance-shaping factors .

To overcome these challenges, Yamo Cao, Zeren Jin, and Assistant Professor Yuguang Fu from Nanyang Technological University proposed a comprehensive framework for data collection and a new hybrid model for prediction. The proposed framework consists of two main components: A Unified Data Collection and Processing Framework and A Hybrid SOM-BN Inference Engine.

The Unified Data Collection and Processing Framework brings together subjective worker self-assessments and objective expert evaluations. To process subjective data, the researchers introduced the Contextual Human Reliability Score (CHRS), which applies fuzzy logic and rank-standardization to worker self-reports, effectively reducing bias and creating a robust input metric. The objective data, consisting of expert observations of worker performance, serves as the independent ground truth for model validation.

The Hybrid SOM-BN Inference Engine uses a two-stage process. First, a Self-Organizing Map (SOM), an unsupervised clustering technique, analyzes the CHRS data to identify complex and non-linear patterns among the various performance conditions. Second, these empirically derived clusters are fed into a Bayesian Network (BN), which performs transparent probabilistic inference to predict the worker's performance control mode (e.g., effective or ineffective).

To validate the framework, the researchers conducted a case study on a construction site, collecting data from 35 workers. The results were compelling: the proposed SOM-BN model demonstrated high accuracy (88.57%) and specificity in its predictions. In stark contrast, traditional CREAM models, when applied to the same dataset, were unable to effectively distinguish between safe and unsafe performance, highlighting their limitations in this real-world context. Furthermore, the use of explainability tools like SHAP provided clear insights into individual worker performance, making the model's predictions actionable.

In the future, the research team plans to expand the dataset to include a more diverse worker population and apply the framework to other industries, further demonstrating its broad applicability in improving human reliability and safety.

This paper ”A new hybrid inference model for human performance reliability prediction: a case study of construction workers” was published in Smart Construction.

Cao Y, Jin Z, Fu Y. A new hybrid inference model for human performance reliability prediction: a case study of construction workers. Smart Constr. 2025(3):0016, https://doi.org/10.55092/sc20250016.

Source from [https://www.eurekalert.org/news-releases/1090734].

Researchers have developed a revolutionary non-invasive method combining 3D laser scanning technology and sensor data time-series analysis for identifying defective water treatment filters, achieving significant reductions in inspection time and labor costs while eliminating operational disruptions. Published in Smart Construction, this breakthrough has the potential to transform water treatment facility maintenance, ensuring safer and more efficient water processing by detecting subsurface structural defects through surface geometric changes and operational anomalies.

image: The multi-dimensional data interpretation framework integrates upside-down 3D laser scanning, SCADA sensor data analysis, and CFD simulation validation, tested on actual water treatment filters during backwash operations. The framework features non-invasive detection through geometric feature analysis and time-series pattern recognition, achieving reliable identification of subsurface structural defects. This system could be applied in smart water treatment facilities for automated filter health monitoring, predictive maintenance, and optimized backwash operations.

Credit: Pengkun Liu/Carnegie Mellon University, Jinghua Xiao/Circular Water Solution LLC, Pingbo Tang/Carnegie Mellon University,

Water treatment filters serve as the final physical barrier for removing suspended solids and pathogens, making their structural integrity crucial for effective water treatment and public health protection. However, traditional filter inspection methods require manual disruption of filter media, are time-consuming and labor-intensive, involving at least two personnel for several hours, and risk overlooking defects due to limited sampling areas. Addressing this challenge, Dr. Pengkun Liu and Professor Pingbo Tang from Carnegie Mellon University, in collaboration with researchers from Circular Water Solution LLC, has developed a state-of-the-art multi-dimensional data interpretation framework that revolutionizes water treatment filter monitoring without operational interruptions.

"This framework design marks a critical advancement in water treatment facility maintenance," explains Professor Pingbo Tang. "Our central hypothesis is that when subsurface structural irregularities disrupt normal backwash operations and flow patterns, they produce both visible surface deformations and distinctive anomalies in sensor data, enabling non-invasive defect detection."

The newly developed framework utilizes upside-down installed 3D laser scanners to capture high-precision geometric changes on filter media surfaces before and after backwash processes. Lead researcher Pengkun Liu emphasizes, "The integration of geometric feature analysis—including roughness, curvature, omnivariance, and planarity—with time-series sensor data significantly enhances detection accuracy while eliminating the need for invasive media disruption."

A major challenge in filter inspection has been the inability to detect subsurface defects like uneven gravel support beds, mud ball formation, or underdrain blockages without disrupting operations. The team addressed this by developing a comprehensive four-module architecture: data acquisition through 3D laser scanning and SCADA sensor monitoring, geometric analysis using advanced clustering methods, time-series analysis of operational parameters, and fusion diagnosis validated by computational fluid dynamics simulations.

The framework, tested extensively at the Shenango Water Treatment Plant in Pennsylvania across six filter units, underwent rigorous validation over multiple operational cycles. Led by Jinghua Xiao from Circular Water Solution, the team demonstrated that the system effectively identifies abnormal filters through surface elevation irregularities, geometric feature variations, and operational parameter anomalies. Their measurements confirmed that defective filters exhibit distinctive patterns: surface elevation differences, higher geometric feature values, longer backwash durations, elevated turbidity levels, and reduced water production rates.

In practical applications, the framework successfully identified Filter 2 as defective, showing consistent surface bulging patterns across multiple scans, abnormal geometric characteristics, and suboptimal operational performance. These findings were validated through CFD simulations, which confirmed that subsurface defects like mud balls or dead zones disrupt uniform flow distribution from bottom drainage pipes, causing uneven surface deformations. The repeatability tests demonstrated high consistency in defect detection, proving the system's reliability in real-world water treatment settings.

"This framework has the potential to significantly impact the development of smart water treatment systems and infrastructure monitoring applications," says co-researcher Jinghua Xiao. "Its non-invasive nature and comprehensive multi-modal approach could lead to safer, more cost-effective, and more reliable maintenance practices for water treatment facilities worldwide."

In addition to water treatment applications, the techniques developed in this framework could inspire innovations in other infrastructure monitoring settings requiring precise subsurface defect detection, such as advanced pipeline inspection, industrial filtration systems, and civil infrastructure health monitoring.

The multi-dimensional data interpretation framework achieves exceptional performance through its innovative combination of 3D geometric analysis and sensor data fusion. It operates effectively with millimeter-level precision in surface change detection and real-time operational parameter monitoring, offering both geometric and time-series anomaly detection capabilities. "This approach establishes quantitative relationships between surface irregularities and subsurface defects, setting a new standard for non-invasive infrastructure inspection," notes Professor Pingbo Tang.

While the team acknowledges the need for expanding the dataset to more water treatment facilities and developing real-time monitoring systems, this study represents a critical step toward more efficient, safer, and more reliable water treatment operations. Future research directions include integrating 3D LiDAR sensors directly into filter structures for continuous monitoring and developing adaptive backwash control systems based on real-time surface geometry analysis.

This paper ”Multi-dimensional data interpretation for defective filter identification” was published in Smart Construction.

Liu P, Xiao J, Tang P. Multi-dimensional data interpretation for defective filter identification. Smart Constr. 2025(2):0014, https://doi.org/10.55092/sc20250014.

Source from [https://www.eurekalert.org/news-releases/1089689].

A team of Italian researchers has uncovered compelling evidence of anomalous radioactive decay in cobalt-57 (Co-57) under ultrasonic stimulation, offering strong experimental support for the Deformed Space-Time (DST) theory. The findings, published by Stefano Bellucci (INFN-Frascati) and Fabio Cardone (ISMN-CNR), suggest that brief ultrasonic exposure can trigger a departure from conventional exponential decay laws, mediated by energy-dependent space-time distortions that violate local Lorentz invariance (LLI).

image: Ultrasound triggers anomalous Co-57 decay via Deformed Space-Time effects. Just nanoseconds of sonication induce non-classical nuclear transformations, offering new evidence for Lorentz Invariance violation and metric-dependent nuclear reactions.

Credit: Stefano Bellucci/INFN-LNF, Italy

In a groundbreaking study exploring the frontier between nuclear physics and space-time geometry, Stefano Bellucci and Fabio Cardone report anomalous radioactive decay behavior in the isotope cobalt-57 (Co-57) when exposed to ultrasound at 2.25 MHz. This unexpected deviation from the standard exponential decay curve—specifically in the 14.4 keV Fe-57 emission line—is interpreted through the lens of Deformed Space-Time (DST) theory, which posits that under certain energy conditions, nuclear processes occur in a locally non-Minkowskian metric.

"Only a few nanoseconds of ultrasonic activation—less than one percent of a single wave cycle—are enough to trigger measurable effects consistent with a deformed space-time," explains Bellucci. These effects include enhanced transformation of Co-57 nuclei without traditional radioactive decay emissions, suggesting an alternative, non-weak-interaction-driven nuclear transformation.

At the heart of the study lies the hypothesis that Ridolfi cavities—microcavities formed under ultrasonic stress—act as "nuclear micro-reactors," enabling strong-interaction pathways not accessible under standard conditions. This dual-path decay mechanism, combining traditional weak decay with DST-induced transformation, offers a radical reinterpretation of nuclear stability and decay in dynamic fields.

Notably, the research draws parallels to previous DST-based experiments conducted by the same team involving isotopes like thorium-228 (Th-228) and nickel-63 (Ni-63), where cavitation led to significant reductions in radioactivity. The new findings from the Hagelstein-type experiment with Co-57 serve as an independent confirmation, revealing persistent metric deformation effects (latency) and energy coupling between fields—phenomena consistent with the so-called Mignani mimicry.

“This work challenges the long-held assumption that radioactive decay is immutable under classical field exposure,” says Cardone. “If space-time itself can deform in response to external stress, our understanding of fundamental interactions—and their constraints—must be revisited.”

The authors emphasize that the observed decay anomalies imply a deeper violation of Local Lorentz Invariance, hinting at broader implications for causality and even the constancy of the speed of light. Future experiments are proposed to determine whether the observed transformations result from an increase in decay rate—or a true metamorphosis of nuclear identity, absent radiation emission.

As a forward-looking application, the authors propose real-time radioactivity monitoring during sonication. “If sonication leads to more radiation, it implies faster decay,” notes Bellucci. “If not, we are looking at a fundamentally different reaction altogether.”

This study opens new avenues in nuclear science, cosmology, and the physics of space-time—where matter, energy, and geometry may interact more dynamically than previously imagined.

This paper ”On anomalous radioactive decay according to the energy metrics formalism in the Deformed Space-Time (DST) theory” was published on 30 June 2025 in ELSP Asymmetry.

Stefano Bellucci and Fabio Cardone, On anomalous radioactive decay according to the energy metrics formalism in the Deformed Space-Time (DST) theory. Asymmetry 2025(1):0005, https://doi.org/10.55092/asymmetry20250005.

Source from [https://www.eurekalert.org/news-releases/1090173].

Floating surface single-junction type photodiodes are mostly used in solar cell applications for simplicity and cost. On the other hand, pinned-surface and double-junction type photodiodes are used now in super high-performance image sensor applications. This paper first reviews the difference between the conventional floating-surface single-junction type photodiode and the pinned-surface double-junction type photodiode. The pinned-surface buried-channel P+PNPP+ double junction type photodiodes are very high-performance image sensors with no image lag and very high light sensitivity compared to conventional ones. The diode can be applied not only to image sensors but also to solar cells. In addition, this paper proposes a new AI robot vision chip in the modern 3DIC CMOS image sensor technology using this double junction type diode. So, the diode will be widely interested in process, device, and application researchers and engineers for image sensors and solar cells. A real-time AI smart robot vision chip is described as an example of application, which is composed of an array of N × N pinned-surface buried-channel P+PNPP+ double junction type photo diodes, N × N analog-data stream mask-and-match comparators, digital processing and SRAM cache buffer memory units, integrated in a 3-D multichip architecture. In the external power-off mode, the image sensor array of N × N pinned-surface buried-channel P+PNPP+ double junction type photo diodes also function as a solar cell unit for the AI self-energy robot vision chip.

image: Single junction type (a) and the proposed double junction type (b) solar cells are compared with the conventional TANDEM type (c) and the proposed face-to-face type (d) multi-junction connections. A cross-sectional view of a new AI Robot Vision chip in 3DIC multichip architecture.(e) shows one-pixel unit of a unique high-performance image sensor unit, with the solar cell capability, which is composed of a pinned-surface buried-channel P+PNPP+ double junction photodiode with an in-pixel source-follower current-amplifier, and also with a depletion MOS type charge transfer gate (CTG) and another depletion MOS type pre-charging gate (PCG) with a switching outlet diffusion drain (ODD) region, for draining out the photo signal electrons.

Credit: Yoshiaki Daimon Hagiwara, President Office, Sojo University, Kumamoto-city, Japan

Charge couple device (CCD) type charge transfer device (CTD), originally invented in 1970, is composed of a series of MOS large-capacitor gates, and consumes a large power for charging and discharging the large CCD/MOS capacitors in order to perform the complete charge transfer operations. Thanks to the advancements in the long history of modern low-power CMOS process scaling technology, CCD type CTD is now completely replaced by the low-power digital CMOS type CTD with the in-pixel source-follower current-amplifier circuit originally invented by Peter Noble in 1969. Since the size of the source-follower current amplifier circuit was too large to be included in each pixel, the buried-channel charge coupled device (CCD) type charge transfer device (CTD) was used till late 1990s.

Thanks to the modern CMOS scaled advanced process technology, in our high-definition digital TV era, MOS transistors and S/D contacts have now become small enough to be included in each pixel of image sensors. Charge Coupled Device (CCD) type was not the only charge transfer device (CTD) that transfers the information of one single photo electron for a long distance in a silicon chip.

Image sensor in general is composed of three basic parts, (1) a light-sensing photodiode, (2) a signal charge transfer device (CTD) and (3) the image information processing unit. Historically, the first original PNP double junction buried-channel type light-sensing photodiode was invented by Philips in Netherland on June 9, 1975, which showed the empty potential well in the buried channel for a single electron to move along for a long distance in the silicon chip. However, the surface P region was connected only to the high-resistivity substrate, with some undesired RC delay time to the substrate pinned ground voltage level. This device works only for low-frequency operations because of the RC delay time constant to the substrate.

This paper explains the pinned-surface completely-majority-carrier-depleted charge-collecting buried-channel/storage region with the complete charge transfer capability for high-frequency operations, realizing the completely-mechanical-free film-less Global and Electrical Shuter functions.

At low frequency, the surface P-region can remain grounded and the buried N-region can be kept completely depleted of the majority carrier electrons. And with no minority carrier hole present for recombination in the buried N-channel region, this PNP double junction photodiode can transfer one single photo electron for a distance inside of the silicon chip without recombination at low frequency.

For high frequency operations, however, the surface hole accumulation region must be pinned in order to function as a pinned virtual gate for realizing the no-image-lag feature with the high-frequency global and electronic shutter function capability, in order to realize high-performance video camera operations, completely free from any mechanical parts and any film media. The difference between the pinned-surface and the floating-surface photodiodes makes a big difference.

High performance pinned-surface double and triple junction photodiodes invented by Hagiwara at Sony in 1975 were hinted by Sony original double and triple junction type bipolar dynamic transistor technology. They had a unique in-pixel vertical overflow drain (VOD) built-in structure with the global and electronic shutter capability, suitable for consumer video camera applications.

Sony filed in 1975 a series of Japanese patent applications on the in-pixel pinned-surface and buried-channel double and triple junction photodiodes, but never disclosed the details until recently. Sony pinned-photodiodes have the unique global and global shutter capability for capturing quick action pictures, free from any mechanical parts and replacing completely the film media by electronic media.

A high-energy ion implantation technology with a unique lamp anneal method was used to fabricate the pinned-surface P+PNPP+ double junction photodiode. Sony reported the complete charge transfer capability and the high quantum efficiency in the SSDM1977 and the SSDM1978 international conferences, and also in another domestic conference in Tokyo. Then, Sony was invited to talk at the CCD1979 conference in Edinburgh, Scotland UK and also at the ECS1980 conference in St. Louis, USA. In 1979,

Hynecek developed a virtual-phase CCD delay line with the complete charge transfer capability with the pinned-surface buried-channel PNP junction photodiode. The pinned-surface of the PNP junction photodiode functions as a virtual gate for the complete charge transfer action.

In 1982 NEC developed the buried-channel photodiode and used it in an ILT CCD type CTD image sensor with detailed measurement data of the image lag. The NEC photodiode, used in the ILT CCD, reported in IEDM1982 was identical with the Philips 1975 invention of the floating-surface PNP double junction buried photodiode. The surface hole accumulation was connected to the high resistivity substrate.

In IEDM1984, Kodak emphasized the importance of the pinning the surface hole accumulation region to achieve the completely-no-image-lag feature. Kodak reported that there was no image lag and named the device as Pinned Photodiode, which is now widely known as the pinned-surface photodiode.

The SSDM1978 conference paper by Sony and the IEDM1984 conference paper by KODAK both reported the excellent short-wave blue-light sensitivity with a very high quantum-efficiency of 60% to 80% on image sensor chips.

The CCD type charge transfer device (CTD) is now completely replaced by the in-pixel source-follower current-amplifier circuit, originally invented in 1969 by Peter Noble, owing to the advancements of the scaled modern digital CMOS process technology.

However, the pinned-surface buried-channel P+PNPP+ double junction photodiode, invented in 1975 by Sony, has been used in the CCD video cameras in the analog TV era and also now widely in CMOS video cameras and smart phones in our modern digital TV era.

The diode can be applied not only to image sensors but also to solar cells. In addition, this paper proposes a new AI robot vision chip in the modern 3DIC CMOS image sensor technology using this double junction type diode. So, the diode will be widely interested in process, device, and application researchers and engineers for image sensors and solar cells.

A real-time AI smart robot vision chip is described as an example of application, which is composed of an array of N × N pinned-surface buried-channel P+PNPP+ double junction type photo diodes, N × N analog-data stream mask-and-match comparators, digital processing and SRAM cache buffer memory units, integrated in a 3-D multichip architecture.

In the external power-off mode, the image sensor array of N × N pinned-surface buried-channel P+PNPP+ double junction type photo diodes also function as a solar cell unit for the AI self-energy robot vision chip.

Hagiwara YD. Pinned-surface and double-junction photodiode type super high-performance image sensor with built-in solar cell structure. Electron. Signal Process. 2025(1):0003, https://doi.org/10.55092/20250003.

Source from [https://www.eurekalert.org/news-releases/1086885].

Published in Smart Construction, this study investigates the cyclic bond behavior of fiber reinforced polymer (FRP) bars—an area vital to seismic design yet previously underexplored. By examining carbon (CFRP), glass (GFRP), and basalt (BFRP) fiber reinforced polymer bars under reversed cyclic loading, the research quantifies how bar diameter, embedment length, concrete strength, and rib geometry influence initial bond stiffness, unloading strength, frictional resistance, and energy dissipation. A unified bond stress–slip constitutive model and hysteresis framework are developed to capture interfacial degradation mechanisms under cyclic loads. These contributions offer key insights for improving the seismic performance and reliability of FRP-reinforced concrete structures in earthquake-prone regions.

image: The study investigates the cyclic bond behavior of FRP bars from four key parameters—bar diameter, embedment length, concrete strength, and rib geometry—leading to the development of a unified bond stress–slip constitutive model and hysteresis framework. Validated through systematic pull-out tests, the model accurately captures interfacial degradation mechanisms under cyclic loading and provides a reliable foundation for simulating FRP-concrete interaction in seismic applications, enhancing the precision of performance-based structural design.

Credit: Bo Li/Beijing University of Technology, Dong Li/Beijing University of Technology, Fengjuan Chen/Beijing University of Technology, Liu Jin/Beijing University of Technology, Xiuli Du/Beijing University of Technology

This study uses physical test methods to systematically study the evolution of bonding performance of different types of fiber reinforced polymer (FRP) bars under cyclic loading. Through positive and negative cyclic pull-out tests on three types of FRP bars, carbon fiber (CFRP), glass fiber (GFRP) and basalt fiber (BFRP), the effects of bar diameter, anchorage length, concrete strength and surface rib shape on bonding stiffness, unloading strength, friction and energy dissipation capacity are comprehensively analyzed.

1. Experimental design

Material type: Three representative FRP bars, CFRP, GFRP and BFRP, are selected to study the differences in their bonding behavior under cyclic loading.

Control parameters: Multiple groups of bar diameter, anchorage length, concrete strength grade and rib shape feature combinations are set to systematically examine the effects of various factors on bonding performance.

Loading method: Apply positive and negative cyclic displacement loading to obtain a complete load-slip hysteresis curve to characterize the degradation characteristics of bonding performance.

2. Key Parameter Analysis

The effects of key influencing parameters—bar diameter, embedment length, concrete compressive strength, and surface rib geometry—were investigated through reversed cyclic pull-out tests to evaluate the bond behavior between FRP bars and concrete. Bond performance indicators such as initial stiffness, unloading strength, frictional resistance, and energy dissipation were analyzed. Main findings include:

Bar diameter: An increase in diameter generally reduces bond stress due to a lower specific surface area, resulting in decreased frictional resistance and energy dissipation under cyclic loading.

Embedment length: Greater embedment length enhances anchorage capacity and improves unloading stiffness, but after a threshold, the bond performance gain becomes marginal.

Concrete compressive strength: Higher concrete strength improves initial stiffness and peak bond strength, while also delaying interface degradation during cyclic loading.

Rib geometry: Well-defined surface ribs significantly enhance mechanical interlock, improving cyclic bond performance; however, overly aggressive ribs may lead to stress concentration and early interface damage.

3. Constitutive Model and Hysteresis Framework

This study reveals that the bond–slip behavior between FRP bars and concrete under cyclic loading is governed by complex interfacial degradation mechanisms, including frictional loss, stiffness reduction, and progressive slip accumulation. Through systematic reversed cyclic pull-out testing, the evolution of bond performance across different FRP types and influencing parameters was quantified.

To capture these mechanisms, a unified bond stress–slip constitutive model was proposed, incorporating distinct loading, unloading, and reloading branches. The model reflects nonlinearity in initial stiffness, residual strength after unloading, and energy dissipation via slip-dependent degradation rules. A corresponding hysteresis framework was developed to describe the full cyclic response, including pinching effects and strength decay over multiple load cycles.

The proposed model significantly improves prediction accuracy for bond behavior under seismic-like loading and serves as a foundational tool for nonlinear simulation of FRP-reinforced concrete structures. It also lays the groundwork for integrating interfacial damage mechanics into performance-based seismic design. Future work will focus on extending the model to full-scale structural elements and validating it against dynamic loading conditions.

This paper “ Bond behavior of FRP bars in concrete under reversed cyclic loading: an experimental study” was published in Smart Construction.

Li B, Li D, Chen F, Jin L, Du X. Bond behavior of FRP bars in concrete under reversed cyclic loading: an experimental study. Smart Constr. 2025(2): 0013, https: //doi. org/ 10. 55092/ sc20250013.

Source from [https://www.eurekalert.org/news-releases/1089658].