This paper presents a methodology-centered transfer learning framework for fragility adaptation under domain shift, class imbalance, and scarce target labels while preserving engineering interpretability and supporting decision-making under uncertainty. Four transfer learning strategies (instance-based, parameter-based, hierarchical Bayesian, and multi-source) are demonstrated through three complementary case studies: (i) instance-based transfer learning via importance weighting, demonstrated on coastal bridge fragility using Hurricane Katrina observations; (ii) parameter-based transfer learning together with hierarchical Bayesian transfer learning, enabling partial pooling across strata and posterior uncertainty quantification, demonstrated on residential building fragility using Hurricane Ian observations; and (iii) multi-source transfer learning that fuses multiple analytical fragility models with learned source weights and regularized target-domain adaptation, demonstrated on seismic bridge fragility using observations from the 2001 Nisqually earthquake. Across these case studies, direct transfer of source models (i.e. using existing state-of-the-art models) fails under domain shift and severe class imbalance, while targeted adaptation substantially improves failure detection and predictive stability in low-data regimes. These findings highlight the need for systematic guidance on diagnostics, strategy selection, and uncertainty reporting when developing and adapting fragility models.

In many engineering applications, a single high-fidelity model produces multiple quantities of interest (QoIs) under the same input parameters, e.g. finite element models of complex physical systems. To alleviate the high computational cost of direct model evaluations, surrogate models are widely used to construct efficient approximations of model responses. Naturally, the accuracy of surrogates strongly depends on the quality of the experimental design (ED). However, a single ED may not provide an adequate representation for all outputs simultaneously, especially when different outputs exhibit varying sensitivities to the input variables. A straightforward solution is to perform separate sampling for each output, but this results in increased sampling complexity and computational cost. From a statistical perspective, such an approach also ignores potential correlations among all outputs and may compromise data consistency. To address this issue, an adaptive sequential sampling method for constructing polynomial chaos expansion surrogate models is generalized for vector valued QoIs. The method sequentially selects new samples from a candidate pool based on their local contribution to the output variance, while balancing distance-based exploration of the input space and exploitation of aggregated variance information across all outputs. Its performance is compared with non-sequential Latin Hypercube Sampling through several numerical examples from engineering problems. Numerical results demonstrate that the proposed strategy improves both surrogate accuracy and stability, and provides a more reliable estimation of second-order statistics.

Robotically assembled building blocks could be a more environmentally friendly method for erecting large-scale structures than some existing construction techniques, according to a new study by MIT researchers. The team conducted a feasibility study to evaluate the efficiency of constructing a simple building using "voxels," which are modular 3D subunits that assemble into complex, durable structures. After studying the performance of multiple voxels, the researchers developed three new designs intended to streamline building construction. They also produced a robotic assembler and a user-friendly interface for generating voxel-based building layouts and feeding instructions to the robots. Their results indicate this voxel-based robotic assembly system could reduce embodied carbon -- all of the carbon emitted during the lifecycle of building materials -- by as much as 82 percent, compared with popular techniques like 3D concrete printing, precast modular concrete, and steel framing.

The performance of machine learning surrogates is critically dependent on data quality and quantity. This presents a major challenge, as high-fidelity (HF) data is often scarce and computationally expensive to acquire, while low-fidelity (LF) data is abundant but less accurate. To address this data-scarcity problem, we develop a probabilistic multi-fidelity surrogate framework based on generative transfer learning. We employ a normalizing flow (NF) generative model as the backbone, which is trained in two phases: (i) the NF is first pretrained on a large LF dataset to learn a probabilistic forward model; (ii) the pretrained model is then fine-tuned on a small HF dataset, allowing it to correct for LF-HF discrepancies via knowledge transfer. To relax the dimension-preserving constraint of standard bijective NFs, we integrate surjective (dimension-reducing) layers with standard coupling blocks. This architecture enables learned dimension reduction while preserving the ability to train with exact likelihoods. The resulting surrogate provides fast probabilistic predictions with quantified uncertainty and significantly outperforms LF-only baselines while using fewer HF evaluations. We validate the approach on a reinforced concrete slab benchmark, combining many coarse-mesh (LF) simulations with a limited set of fine-mesh (HF) simulations. The proposed model achieves probabilistic predictions with HF accuracy, demonstrating a practical path toward data-efficient, generative AI-driven surrogates for complex engineering systems. Email address: David.Barajas-Solano@pnnl.gov (David Barajas-Solano) Introduction High-fidelity (HF) computer modeling using discretization schemes such as the finite elements (FE) method provides a rigorous framework for analyzing and predicting the behavior of complex engineering systems.

Improved carbon-cement supercapacitors could turn the concrete around us into massive energy storage systems. Concrete already builds our world, and an MIT-invented variant known as electron-conducting carbon concrete (ec, pronounced "e c cubed") holds out the possibility of helping power it, too. Now that vision is one step closer. Made by combining cement, water, ultra-fine carbon black, and electrolytes, ec creates a conductive "nanonetwork" that could enable walls, sidewalks, and bridges to store and release electrical energy like giant batteries. To date, the technology has been limited by low voltage and scalability challenges. But the latest work by the MIT team that invented ec has increased the energy storage capacity by an order of magnitude.

This study develops a forecasting framework t hat leverages the Construction Specifications Institute (CSI) MasterFormat as the target data structure, enabling predictions at the six - digit section level and supporting detailed cost projections across a wide spectrum of building materials. To enhance p redictive accuracy, the framework integrates explanatory variables such as raw material prices, commodity indexes, and macroeconomic indicators. Four time - series models, Long Short - Term Memory (LSTM), Autoregressive Integrated Moving Average (ARIMA), Vecto r Error Correction Model (VECM), and Chronos - Bolt, were evaluated under both baseline configurations (using CSI data only) and extended versions with explanatory variables. Results demonstrate that incorporating explanatory variables significantly improves predictive performance across all models. Among the tested approaches, the LSTM model consistently ach ieved the highest accuracy, with RMSE values as low as 1.390 and MAPE values of 0.957, representing improvements of up to 59 % over traditional statistical time - series model, ARIMA. Validation across multiple CSI divisions confirmed the framework's scalability, while Division 06 (Wood, Plastics, and Composites) is presented in detail as a demonstration case. This research offers a robust methodology that enables owners and contractors to improve budgeting practices and achieve more reliable cost estimation at the Definitive level. INTRODUCTION 1.1 Motivation The construction industry continues to demonstrate steady long - term growth, with global activity projected to reach US$9.8 trillion by 2026 [1] . Major upcoming programs in the United States, such as the Los Angeles 2028 Olympics and TSMC's fabrication facility in Arizona [2] [3], highlight the scale of high - value projects in the near future. However, volatility in construction material prices has emerged as a critical challenge, creating significant uncertainty for contractors in project planning, budgeting, and cost management. Price fluctuations, driven by raw material costs, macroeconomic conditions such as inflation and interest rates, and supply - demand imbalances, have amplified risks of cost overruns and delays [4] [5] [6] [7] [8] . Traditional econometric methods (i.e.,multiple regression analysis) and modern econometric methods (i.e., univariate, and multivariate time series methods) have faced limitations in effectively capturing the high - frequency volatility observed in constructi on material prices [9] . These models often struggle to handle the complexity of input data and exhibit limited predictive accuracy in real - world applications.

High-performance concrete requires complex mix design decisions involving interdependent variables and practical constraints. While data-driven methods have improved predictive modeling for forward design in concrete engineering, inverse design remains limited, especially when some variables are fixed and only the remaining ones must be inferred. This study proposes a cooperative neural network framework for the partial inverse design of high-performance concrete. The framework integrates an imputation model with a surrogate strength predictor and learns through cooperative training. Once trained, it generates valid and performance-consistent mix designs in a single forward pass without retraining for different constraint scenarios. Compared with baseline models, including autoencoder models and Bayesian inference with Gaussian process surrogates, the proposed method achieves R-squared values of 0.87 to 0.92 and substantially reduces mean squared error by approximately 50% and 70%, respectively. The results show that the framework provides an accurate and computationally efficient foundation for constraint-aware, data-driven mix proportioning.

Kolmogorov-Arnold Networks (KANs) offer a promising path toward interpretable machine learning: their learnable activations can be studied individually, while collectively fitting complex data accurately. In practice, however, trained activations often lack symbolic fidelity, learning pathological decompositions with no meaningful correspondence to interpretable forms. We propose Softly Symbolified Kolmogorov-Arnold Networks (S2KAN), which integrate symbolic primitives directly into training. Each activation draws from a dictionary of symbolic and dense terms, with learnable gates that sparsify the representation. Crucially, this sparsification is differentiable, enabling end-to-end optimization, and is guided by a principled Minimum Description Length objective. When symbolic terms suffice, S2KAN discovers interpretable forms; when they do not, it gracefully degrades to dense splines. We demonstrate competitive or superior accuracy with substantially smaller models across symbolic benchmarks, dynamical systems forecasting, and real-world prediction tasks, and observe evidence of emergent self-sparsification even without regularization pressure.

The industrial adoption of Artificial Intelligence for Engineering (AI4E) faces two fundamental bottlenecks: scarce high-quality data and the lack of interpretability in black-box models-particularly critical in safety-sensitive sectors like aerospace. We present an explainable, few-shot AI4E framework that is systematically informed by physics and expert knowledge throughout its architecture. Starting from only 32 experimental samples in an aerial K439B superalloy castings repair welding case, we first augment physically plausible synthetic data through a three-stage protocol: differentiated noise injection calibrated to process variabilities, enforcement of hard physical constraints, and preservation of inter-parameter relationships. We then employ a nested optimization strategy for constitutive model discovery, where symbolic regression explores equation structures while differential evolution optimizes parameters, followed by intensive parameter refinement using hybrid global-local optimization. The resulting interpretable constitutive equation achieves 88% accuracy in predicting hot-cracking tendency. This equation not only provides quantitative predictions but also delivers explicit physical insight, revealing how thermal, geometric, and metallurgical mechanisms couple to drive cracking-thereby advancing engineers' cognitive understanding of the process. Furthermore, the constitutive equation serves as a multi-functional tool for process optimization and high-fidelity virtual data generation, enabling accuracy improvements in other data-driven models. Our approach provides a general blueprint for developing trustworthy AI systems that embed engineering domain knowledge directly into their architecture, enabling reliable adoption in high-stakes industrial applications where data is limited but physical understanding is available.

Accurate and efficient seismic response prediction is essential for the design of resilient structures. While the Finite Element Method (FEM) remains the standard for nonlinear seismic analysis, its high computational demands limit its scalability and real time applicability. Recent developments in deep learning, particularly Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short Term Memory (LSTM) models, have shown promise in reducing the computational cost of nonlinear seismic analysis of structures. However, these data driven models often struggle to generalize and capture the underlying physics, leading to reduced reliability. We propose a novel Physics Informed U Net LSTM framework that integrates physical laws with deep learning to enhance both accuracy and efficiency. By embedding domain specific constraints into the learning process, the proposed model achieves improved predictive performance over conventional Machine Learning architectures. This hybrid approach bridges the gap between purely data driven methods and physics based modeling, offering a robust and computationally efficient alternative for seismic response prediction of structures.