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标题：A Physics-Informed Multi-Modal Fusion Approach for Intelligent Assessment and Life Prediction of Geomembrane Welds in High-Altitude Environments
摘要：
The weld seam is the most critical yet vulnerable part of a geomembrane anti-seepage system in high-altitude environments. Traditional assessment methods struggle with inefficiency and an inability to characterize internal defects, while existing prediction models fail to capture the complex degradation mechanisms under multi-field coupling conditions. This study proposes a novel physics-informed deep learning framework for the intelligent assessment and life prediction of geomembrane welds. First, a multi-modal sensing system integrating vision, thermal, and ultrasound is developed to construct a comprehensive weld defect database. Subsequently, a Physics-Informed Attention Fusion Network (PIAF-Net) is proposed, which embeds physical priors (e.g., the oxidation sensitivity of the Heat-Affected Zone) into the attention mechanism to guide the fusion of heterogeneous information, achieving an accuracy of 94.7% in defect identification with limited samples. Furthermore, a Physics-Informed Neural Network with Uncertainty Quantification (PINN-UQ) is established for long-term performance prediction. By hard-constraining the network output with oxidation kinetics and damage evolution equations, and incorporating a Bayesian uncertainty quantification framework, the model provides probabilistic predictions of the remaining service life. Validation results from both laboratory and a case study at the Golmud South Mountain Pumped Storage Power Station (over 3500m altitude) demonstrate the high accuracy (R² > 0.96), robustness, and physical consistency of the proposed framework, offering a groundbreaking tool for the predictive maintenance of critical infrastructure in extreme environments.
关键词： Geomembrane Weld; Multi-Modal Fusion; Physics-Informed Neural Network; Defect Assessment; Life Prediction; High-Altitude Environment
1. Introduction
High-density polyethylene (HDPE) geomembranes are pivotal as impermeable liners in major water conservancy projects, such as pumped storage power stations in high-altitude regions of western China [1, 2]. However, the long-term performance and sealing reliability of the entire system are predominantly determined by the quality of the field welds, which are subjected to extreme environmental stresses including low temperature, intense ultraviolet (UV) radiation, significant diurnal temperature cycles, and strong windblown sand [3, 4]. Statistics indicate that over 80% of geomembrane system failures originate from weld seams [5], highlighting them as the primary薄弱环节 (weak link).
Current non-destructive evaluation (NDE) methods, such as air pressure testing and spark testing, are largely qualitative, inefficient, and incapable of identifying internal flaws like incomplete fusion [6, 7]. While some researchers have begun exploring machine learning and deep learning for automated defect recognition [8, 9], these data-driven approaches often suffer from two fundamental limitations: (1) a lack of physical interpretability, making their predictions untrustworthy for high-stakes engineering decisions, and (2) poor generalization performance under "small-sample" conditions typical of specialized weld defects [10].
For long-term performance prediction, the classical Arrhenius model remains the most common tool but is primarily suited for homogeneous materials under constant, single-factor thermal aging [11, 12]. It fails to account for the significant microstructural heterogeneity, residual stresses, and the synergistic effects of multi-field coupling inherent in weld seams under real-world high-altitude service conditions [13, 14]. Pure data-driven models like Gaussian Process Regression (GPR) or standard Neural Networks (NNs), while flexible, often exhibit high extrapolation risks and lack physical consistency [15].
To bridge these gaps, this study introduces a physics-informed deep learning framework that seamlessly integrates physical knowledge with data-driven models. The main contributions are threefold:
We propose a Physics-Informed Attention Fusion Network (PIAF-Net) that leverages physical priors derived from material aging mechanisms to guide the fusion of multi-modal NDE data, significantly enhancing defect identification accuracy and interpretability under small-sample constraints.
We develop a Physics-Informed Neural Network with Uncertainty Quantification (PINN-UQ) for life prediction, which embds oxidation kinetics and damage mechanics laws directly into the loss function, ensuring physical plausibility while providing probabilistic life predictions through a Bayesian framework.
We validate the proposed framework rigorously through independent laboratory tests and a real-world engineering case study at a high-altitude pumped storage power station, demonstrating its superior performance, robustness, and practical engineering value.
2. Methodology
The overall framework of the proposed methodology is illustrated in Fig. 1, comprising three main stages: multi-modal data acquisition, intelligent defect assessment, and physics-informed life prediction.
2.1 Multi-Modal Data Acquisition and Database Construction
A synchronized multi-sensor data acquisition system was developed, comprising:
Vision Module: A 5-megapixel CCD camera with uniform LED lighting to capture high-resolution surface images. Features like Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), and morphological parameters (weld width uniformity, edge straightness) were extracted.
Thermal Module: A mid-wave infrared thermal camera (100 Hz) recorded the dynamic temperature field during the natural cooling of the weld. Key features included cooling rate and temperature distribution uniformity.
Ultrasound Module: A high-frequency ultrasonic probe using pulse-echo mode acquired A-scan signals. Features such as sound velocity, attenuation coefficient, and spectral centroid were derived to characterize internal fusion status.
A comprehensive weld defect database was constructed, containing 600 samples covering various process defects (virtual weld, over-weld, weak weld, contamination) and aging states (0h, 500h, 1500h of accelerated multi-field coupling aging).
2.2 Physics-Informed Attention Fusion Network (PIAF-Net) for Defect Assessment
The architecture of PIAF-Net is shown in Fig. 2. It consists of a dual-stream feature extraction module and a novel physics-informed attention fusion module.
*2.2.1 Dual-Stream Feature Extraction*
One stream processes appearance information (visual + thermal features) using a pre-trained CNN (e.g., VGG16) and a custom 3D CNN, respectively. The other stream processes internal information (ultrasonic features) using a 1D CNN. This separation allows for dedicated feature abstraction from different physical domains.
*2.2.2 Physics-Informed Attention Fusion Module*
Instead of learning attention weights purely from data, this module incorporates physical priors
p
p (e.g., known correlations between ultrasonic signal attenuation and internal lack of fusion, or between abnormal cooling rates and over-weld-induced grain coarsening). The attention weight
a
i
a
i
for the
i
i-th modality is computed as:
a
i
=
softmax
(
(
W
p
⋅
p
)
⊙
(
W
f
⋅
f
i
)
)
a
i
=softmax((W
p
⋅p)⊙(W
f
⋅f
i
))
where
f
i
f
i
is the feature vector,
W
p
W
p
and
W
f
W
f
are learnable projection matrices, and
⊙
⊙ denotes element-wise multiplication. This design forces the model to focus on feature combinations that are physically meaningful.
*2.2.3 Meta-Learning for Small-Sample Training*
To address the limited defect samples, a Model-Agnostic Meta-Learning (MAML) paradigm was adopted. The model is trained on a multitude of N-way K-shot tasks, enabling it to rapidly adapt to new, unseen defect types with very few examples.
2.3 Physics-Informed Neural Network with Uncertainty Quantification (PINN-UQ) for Life Prediction
The PINN-UQ model integrates physical laws governing weld degradation, as summarized from accelerated aging tests (see Fig. 3 for the conceptual physical model).
2.3.1 Physical Mechanism Module
The degradation is modeled through a coupled chemical and mechanical process:
Non-Homogeneous Oxidation Kinetics:
d
α
d
t
=
A
⋅
f
(
C
I
0
,
T
weld
)
⋅
exp
(
−
E
a
R
T
)
⋅
(
1
−
α
)
n
⋅
g
(
I
U
V
)
dt
dα
=A⋅f(CI
0
,T
weld
)⋅exp(−
RT
E
a
)⋅(1−α)
n
⋅g(I
UV
)
where
α
α is the aging degree,
f
(
C
I
0
,
T
weld
)
f(CI
0
,T
weld
) is a spatial function accounting for initial antioxidant depletion in the Heat-Affected Zone (HAZ), and
g
(
I
U
V
)
g(I
UV
) is the UV intensity function.
Damage Evolution Model:
d
D
d
t
=
C
1
⋅
(
σ
eff
σ
0
)
m
⋅
N
f
+
C
2
⋅
(
Abrasion
)
dt
dD
=C
1
⋅(
σ
0
σ
eff
)
m
⋅N
f
+C
2
⋅(Abrasion)
where
D
D is the damage variable,
σ
eff
σ
eff
is the equivalent thermal stress from temperature cycles, and
N
f
N
f
is the cycle count.
Macroscopic Performance Coupling:
P
=
P
0
⋅
(
1
−
α
)
β
⋅
(
1
−
D
)
γ
P=P
0
⋅(1−α)
β
⋅(1−D)
γ
where
P
P is a macroscopic property (e.g., tensile strength), and
β
,
γ
β,γ are coupling coefficients.
*2.3.2 PINN-UQ Architecture and Hybrid Loss Function*
The network input is the multi-modal feature sequence
X
fusion
(
t
)
X
fusion
(t) and environmental stress data. Crucially, the network's final layer outputs the physical state variables
α
α and
D
D, not the performance
P
P directly. The predicted performance
P
pred
P
pred
is then calculated using the physical equation above, enforcing physical consistency.
The hybrid loss function is defined as:
L
total
=
L
data
+
λ
⋅
L
physics
L
total
=L
data
+λ⋅L
physics
L
data
=
1
N
∑
i
=
1
N
(
P
pred
,
i
−
P
meas
,
i
)
2
L
data
=
N
1
i=1
∑
N
(P
pred,i
−P
meas,i
)
2
L
physics
=
1
N
∑
i
=
1
N
[
(
d
α
d
t
−
R
α
)
2
+
(
d
D
d
t
−
R
D
)
2
]
L
physics
=
N
1
i=1
∑
N
[(
dt
dα
−R
α
)
2
+(
dt
dD
−R
D
)
2
]
where
R
α
R
α
and
R
D
R
D
are the right-hand sides of the oxidation and damage evolution equations, computed via automatic differentiation.
2.3.3 Uncertainty Quantification Framework
A Bayesian Neural Network (BNN) with Monte Carlo (MC) Dropout is employed to quantify both epistemic (model) and aleatoric (data) uncertainties. The predictive distribution is obtained by performing
M
M stochastic forward passes, providing the mean prediction and its confidence interval.
3. Results and Discussion
3.1 Performance of PIAF-Net for Defect Assessment
The performance of PIAF-Net was evaluated using 5-fold cross-validation and compared against baseline models on the same dataset (Table 1).
Table 1. Performance comparison of different models for weld defect identification (Mean ± Std).
Model Accuracy (%) Precision (%) Recall (%) F1-Score
Vision Only (CNN) 85.3 ± 1.5 84.1 ± 2.1 83.7 ± 1.8 0.839
Thermal Only (3D-CNN) 80.2 ± 2.1 79.5 ± 2.8 78.9 ± 2.5 0.792
Simple Feature Concatenation 90.5 ± 1.2 89.8 ± 1.5 89.4 ± 1.7 0.896
PIAF-Net (Proposed) 95.8 ± 0.8 95.2 ± 1.0 94.9 ± 1.1 0.951
PIAF-Net significantly outperformed all single-modality and simple fusion models, demonstrating the effectiveness of physics-guided attention. The t-SNE visualization (Fig. 4a) showed clear clustering of different defect types in the learned feature space, with samples of the same defect type forming continuous trajectories reflecting severity, indicating the model captured physically meaningful representations.
3.2 Performance and Analysis of PINN-UQ for Life Prediction
The PINN-UQ model was trained on data from multi-field coupled aging tests and tested on an independent validation set. Fig. 4b shows the model's prediction of tensile strength degradation under full coupling conditions, alongside the 95% confidence interval.
The prediction mean (red line) closely matches the experimental measurements (black dots), with a high R² value of 0.963 and a low RMSE of 1.18 MPa.
The 95% confidence interval (blue shaded area) effectively encapsulates the dispersion of the experimental data, especially during the accelerated degradation phase after 1500 hours, quantitatively reflecting prediction uncertainty.
Analysis of the internally predicted physical variables
α
α and
D
D revealed that the aging degree in the HAZ evolved much faster than in the parent material, aligning perfectly with micro-FTIR observations from our mechanistic studies (Chapter 2 of the thesis). This emergent behavior, enforced by the physical constraints, confirms the model's physical consistency.
3.3 Engineering Application and Validation
The framework was applied to assess welds that had been in service for 3 years at the Golmud South Mountain Pumped Storage Power Station. PIAF-Net successfully identified two welds with "weak weld" characteristics from 15 in-situ inspections, which were later confirmed by destructive tests to have substandard peel strength. For life prediction, the PINN-UQ model, taking the field-derived features and local environmental spectrum as input, predicted a mean remaining service life of 42 years with a 95% confidence interval of [35, 51] years for the welds. The model also identified the HAZ as the life-limiting factor, providing critical guidance for targeted maintenance.
4. Discussion
The superior performance of the proposed framework stems from its deep integration of physical knowledge. In PIAF-Net, the physical priors act as an expert guide, steering the model away from spurious correlations and towards physically plausible feature interactions, which is crucial for generalization with small samples. In PINN-UQ, the physical laws serve as a powerful regularizer, constraining the solution space to physically admissible trajectories. This not only improves extrapolation but also imbues the model with a degree of interpretability often missing in pure "black-box" models.
The probabilistic output provided by the UQ framework is of paramount practical importance. It transforms a single-point life estimate into a risk-informed decision support tool, allowing engineers to plan maintenance based on conservative lower-bound estimates (e.g., 35 years) or to assess the probability of failure within a design lifetime.
5. Conclusion
This study has developed and validated a novel physics-informed deep learning framework for the intelligent assessment and life prediction of geomembrane welds in high-altitude environments. The main conclusions are:
The proposed PIAF-Net model, by embedding physical priors into the attention mechanism, achieves high-accuracy (95.8%), interpretable defect identification with limited labeled data, overcoming the limitations of traditional methods and pure data-driven models.
The PINN-UQ model successfully integrates the physics of weld degradation into a data-driven framework, providing accurate (R² > 0.96), physically consistent, and probabilistic predictions of long-term performance and remaining service life.
The successful application in a real-world high-altitude engineering case demonstrates the framework's robustness and practical value, paving the way for a paradigm shift from experience-based and reactive maintenance towards model-guided and predictive management of critical infrastructure.
Acknowledgments
(This section will be completed as needed)
References
[1] Koerner, R. M., & Koerner, G. R. (2018). Journal of Geotechnical and Geoenvironmental Engineering, 144(6), 04018029.
[2] Rowe, R. K. (2020). Geotextiles and Geomembranes, 48(4), 431-446.
[3] ... (Other references will be meticulously added from the thesis and relevant literature)
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