Structural systems are critical components of modern societies, and as such, need constant condition assessments. Incurred damage and its accumulation due to various loading conditions may lead to complete structural failures with safety consequences. Thus, damage assessment and early fault detection are essential to decision-makers for strategic planning, performing resource-allocation, and obtaining the logistics of these systems. In this article, a data-driven methodology using fault detection of structural systems is proposed. This method utilizes Artificial Neural Networks (ANNs) to model damage due to earthquake loading using sophisticated nonlinear analyses of structures. As a proof-of-concept, the ANN approach for damage-detection is applied to a typical four-story reinforced concrete (RC) structure having varied concrete strengths. The approach is found to have a high potential for successful anomaly identification in RC structural systems.

The probabilistic study of fire exposed structures is laborious and computationally challenging, especially when using advanced numerical models. Moreover, fragility curves developed through traditional approaches apply only to a particular design (structural detailing, fire scenario). Any alteration in design necessitates the computationally expensive re-evaluation of the fragility curves. Considering the above challenges, the use of surrogate models has been proposed for the probabilistic study of fire exposed structures. Previous contributions have confirmed the potential of surrogate models for developing fragility curves for single structural members including reinforced concrete slabs and columns. Herein, the potential of regression-based surrogate models is investigated further with consideration of structural systems. Specifically, an advanced finite element model for evaluating the fire performance of a composite slab panel acting in tensile membrane action is considered. A surrogate model is developed and used to establish fire fragility curves. The results illustrate the potential of surrogate modeling for probabilistic structural fire design of composite structures.

This paper presents the development of a data-driven deep neural network (DNN) for evaluating the fire resistance time of fiber-reinforced polymer (FRP) strengthened concrete beams. The model was trained for a scaled and unscaled dataset. For this, a comprehensive dataset of FRP-strengthened concrete beams with different geometry, insulation configuration, applied loading, and material characteristics was compiled. The DNN structure was selected after an extensive hyperparameter tuning in conjunction with ten-fold cross-validation scheme. The effect of different input parameters on the fire resistance prediction was analyzed. The DNN model developed using scaled data provides a reasonably accurate estimate, of the fire resistance of FRP-strengthened concrete beams with an R2 value of almost 92%. The developed model is further utilized to evaluate the impact of different parameters on fire resistance prediction for FRP-strengthened concrete beams. Results from the analysis indicate the thermal properties of insulation play an important role in determining the fire resistance of FRP-strengthened concrete beams.

This paper presents and explains an implementation of artificial intelligence for the real-time crack detection of ultra-high-performance concrete (UHPC). A deep learning algorithm is employed to process image data and to identify physical cracks. The state-of-the-art object detection method generates accurate results with small datasets. To provide training and validation images, UHPC specimens are cast with various fibers and loaded per an ASTM standard, including steel and synthetic (collated and monofilament polypropylene) fibers. After testing, sample images are labeled with an annotation tool and the algorithm is trained and validated with an image recognition approach, leading to a mean average precision (mAP) of 99%. The occurrence of cracking and propagation are linked with the applied load level to appraise the influence of the mixed fibers in the crack development of UHPC. It needs to be noted that the adopted deep learning architecture is incapable of quantifying crack width and area directly; therefore, a Java-based image processing program is used to measure these properties of the specimens. The characteristics of the load-induced cracks are dominated by the fiber types. Plain UHPC fails rapidly and the flexural capacity of UHPC increases with the presence of the fibers; especially, the UHPC with steel fibers demonstrates higher flexural capacities than other cases.

Many modeling approaches in engineering are based on physical principles. The input and output relationships are developed using physical laws (e.g., Newton's laws of motion and conservation of mass and energy). However, in many situations, the development of physically-based models requires simplifying assumptions due to the complicated nature of the systems, which could lead to a large degree of uncertainty. In these situations, data can be used to formulate models by detecting relationships between the system’s variables (inputs and outputs) without explicitly knowing the physical behavior of the system. Therefore, there is a paradigm shift from physically-based models to data-driven models. The objective of this study is to develop a drift capacity prediction model for structural walls with special boundary elements using the extreme gradient boosting (XGBoost) machine learning algorithm. The resulting prediction model is compared with the recently developed empirical model presented in literature i.e., the Abdullah & Wallace (2019) model. The results reveal the proposed model’s superior predictive capabilities relative to the empirical model.