This study proposes a novel concept of reinforcement learning (RL) framework to facilitate automated structural design, with a particular focus on reinforced concrete (RC) beam design as case studies. We trained an RL agent called deep deterministic policy gradient (DDPG) with a convolutional neural network function approximators. The RL agent was successfully trained to design RC beams subject to American Concrete Institute (318) provisions without any hand-labeled dataset. A python-based RC beam design environment was developed and used to simulate RC beam designs with customized reward functions that encouraged the agent to minimize material cost by maximizing reward. The DDPG agent self-learned cost-effective RC beam design through 100,000 randomly generated design cases during the training procedure. The trained agent was able to design an RC beam in a cost-effective way while taking both flexural and shear reinforcement arrangements into consideration. The trained agent generated near-optimal design parameters without the need for unnecessary iterations over various design conditions. The performance of the agent was validated with 100 design cases and comparative studies, showing a great promise for automated RC beam design.

Road roughness is a measure of how uncomfortable a ride is, and provides an important indicator for the needs of roadway maintenance or repavement, which is closely tied to the state and federal budget prioritization. As such, accurate and timely monitoring of deteriorating road conditions and following maintenance are essential to improve the overall ride quality on the road. Various technologies, including vehicle‐mounted laser profiling systems, have been developed and adopted for road roughness (e.g., IRI—International Roughness Index) measurement; however, their high cost limits their use. While recent advances in smartphone technologies allow us to use their embedded accelerometers for road roughness monitoring, the complicated process of necessary vehicle calibration hinders the widespread use of the technology in the actual practices. In this work, a deep learning IRI estimation method is proposed with the goal of using anonymous (i.e., calibration‐free) vehicles and their responses measured by smartphones as road roughness sensors. A state‐of‐the‐art deep learning algorithm (i.e., CNN—convolutional neural network) and multimetric vehicle dynamics data (i.e., accelerometer, gyroscope), possibly measured by drivers’ smartphones, are employed for the purpose. Optimized CNN architecture and data configuration have been investigated to achieve the best performance. The efficacy of the proposed method has been numerically validated using real road IRI information (i.e., Speedway, Tucson, AZ), real driving speed profiles, and four different types of vehicle data with associated uncertainties.

This paper presents a field implementation of the structural health monitoring (SHM) of fatigue cracks for steel bridge structures. Steel bridges experience fatigue cracks under repetitive traffic loading, which pose great threats to their structural integrity and can lead to catastrophic failures. Currently, accurate and reliable fatigue crack monitoring for the safety assessment of bridges is still a difficult task. On the other hand, wireless smart sensors have achieved great success in global SHM by enabling long-term modal identifications of civil structures. However, long-term field monitoring of localized damage such as fatigue cracks has been limited due to the lack of effective sensors and the associated algorithms specifically designed for fatigue crack monitoring. To fill this gap, this paper proposes a wireless large-area strain sensor (WLASS) to measure large-area strain fatigue cracks and develops an effective algorithm to process the measured large-area strain data into actionable information. The proposed WLASS consists of a soft elastomeric capacitor (SEC) used to measure large-area structural surface strain, a capacitive sensor board to convert the signal from SEC to a measurable change in voltage, and a commercial wireless smart sensor platform for triggered-based wireless data acquisition, remote data retrieval, and cloud storage. Meanwhile, the developed algorithm for fatigue crack monitoring processes the data obtained from the WLASS under traffic loading through three automated steps, including (1) traffic event detection, (2) time-frequency analysis using a generalized Morse wavelet (GM-CWT) and peak identification, and (3) a modified crack growth index (CGI) that tracks potential fatigue crack growth. The developed WLASS and the algorithm present a complete system for long-term fatigue crack monitoring in the field. The effectiveness of the proposed time-frequency analysis algorithm based on GM-CWT to reliably extract the impulsive traffic events is validated using a numerical investigation. Subsequently, the developed WLASS and algorithm are validated through a field deployment on a steel highway bridge in Kansas City, KS, USA.

In recent years, acrlong MCS has emerged as an effective way of collecting essential information about our urban infrastructure integrity conditions. As such, smartphone sensor data obtained while driving vehicles has been widely investigated for road condition monitoring, hoping it can be used as a cost-effective alternative to conventional methods which uses high-cost system, including inertial profilers. However, currently available smartphone methods require expensive signal processing to address various practical uncertainty issues, such as unknown mechanical characteristics of vehicles, variable driving speed, and sensor location. Hence, only a precisely calibrated setup of the vehicle and smartphone could be used for road condition monitoring under a limited environment. The authors’ prior study has developed a deep-learning-based road roughness monitoring method that estimates the international roughness index (IRI) from the anonymous vehicles’ vibrations at any driving speeds, measured by smartphones. The feasibility of the proposed method has been numerically validated. Building on prior efforts, this study investigates its full-scale experimental validation under a real-world environment, addressing associated critical challenges in the real-world implementation of the proposed method. A fully convolutional neural network (CNN) architecture, called IRI-Net, is developed in this study. A new data management strategy is proposed to deal with the low-resolution characteristics of crowdsourced smartphone GPS data. A large-scale data set is collected with total 29 different vehicles under a real-world uncertain environment. The performance of the proposed method is validated by comparing to the reference IRI data obtained from a conventional inertial profiler, showing a great step toward real-world practical applications.

Conventional resistive-type strain sensing methods have limitations in large-area sensing due to their relatively small size. The soft elastomeric capacitive (SEC) sensor is a capacitance-based stretchable electronic strain sensor, which has shown distinct advantages for mesoscale sensing over conventional strain-based structural health monitoring (SHM) due to its wide surface coverage capability. While recent advances in wireless sensor technologies have provided an attractive alternative to wired and centralized SHM, the capacitive strain sensing methods have not benefitted from the wireless approaches due to the lack of appropriate hardware element. This study develops a wireless sensor board to use the SEC sensor in combination with a wireless sensor network for SHM by addressing key implementation challenges. An alternating current (AC)-based De-Sauty Wheatstone bridge circuit is employed, converting dynamic capacitance variation from the SEC sensor into analog voltage signal. A high-precision bridge balancer and two-step signal amplifiers are implemented to effectively apply for low-level structural strain vibrations. An amplitude modulation-demodulator has been designed to extract the baseband signal (i.e. strain signal) from the carrier signal (i.e. AC excitation for the Wheatstone bridge). And a dual-step shunt calibrator has been proposed to remove the parasitic capacitance effect of lead wires during on-board calibration process. The performances of the sensor board developed in this study have been validated via a series of lab tests, outperforming a conventional wired capacitance measurement system.