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This paper presents game theory‐based strategies incorporated with metaheuristics to improve the computation efficiency and effectiveness in design optimization of civil engineering structures. The idea is that a structural optimization problem can be treated as a multiplayer game with optimization algorithms as the players. This study presents an innovative mixed game strategy and provides insights into domain decomposition schemes. Based on game theory strategies, six modified metaheuristic algorithms are proposed and demonstrated through a case study of a 30‐story mega‐braced frame‐core building with a complex configuration. Jaya algorithm is selected as the control to evaluate the proposed methods. The results show that the mixed game strategy helps reduce the computation cost by about 50%, while reducing the weights of the building by 4%, compared with the Jaya algorithm. [ABSTRACT FROM AUTHOR]

Distance measurement is significant for ensuring the safety and serviceability of engineering structures. Recently, ultra-wideband (UWB) sensors have offered an alternative real-time remote sensing solution to measure distances. UWB sensors exhibit small size, low cost, low energy consumption, and high robustness to weather conditions, but their ranging accuracy is still limited. This paper presents two machine learning approaches to achieve high accuracy and high frequency, simultaneously. The first approach integrates a convolutional neural network, a long short-term memory module, and a regression module. The second approach integrates two random forest models. These two approaches were implemented into measurement from UWB sensors deployed on a highway bridge and outperformed the state-of-the-art approaches in terms of measurement accuracy and output frequency. The configurations and key parameters of the two approaches were evaluated and improved. This research enhances the capability of measuring distances and deformations for structural health monitoring. [Display omitted] • A real-time remote measurement method of distance using UWB sensors is developed. • A machine learning approach is presented based on a long short-term memory (LSTM) model. • A machine learning approach is presented based on random forest models. • The two machine learning approaches achieve sub-millimeter measurement accuracy. • The upper frequency of distance measurement reaches 100 Hz using the LSTM-based model. [ABSTRACT FROM AUTHOR]

Distributed fiber optic sensors have exhibited superior capabilities in monitoring cracks in engineering structures through measuring detailed strain distributions. However, manually interpreting the measurements from long distributed sensors deployed in large-scale structures is time-consuming, labor-intensive, and subject to human errors. This paper proposes to automate the identification, localization, quantification, and visualization of cracks through intelligent interpretation of strain distributions measured from distributed fiber optic sensors based on machine learning. Based on these intelligent capabilities, a live digital twin model based on building information modeling is developed to visualize cracks. The digital twin model is updatable with real-time measurements from strain distributions from distributed fiber optic sensors. The proposed approach is evaluated via laboratory testing of a concrete beam. The results show that the proposed approach achieves high accuracy in interpretation of sensor data for crack monitoring. This research advances the capabilities of structural health monitoring using distributed fiber optic sensors. [Display omitted] • Automatic interpretation of strain distributions from distributed fiber optic sensors is enabled. • Machine learning approaches are presented to identify, locate, quantify, and visualize cracks. • A digital twin model is developed to visualize cracks and updated by real-time measurement. • Unsupervised and supervised learning approaches achieve high accuracy and high efficiency. [ABSTRACT FROM AUTHOR]

[Display omitted] Recent advances in robotics and machine vision enable automated measurement of vibration for civil engineering structures. Various types of robotic platforms such as drones and land robots instrumented with advanced navigation and data acquisition systems were developed. Particularly, various cameras have been deployed on robotic platforms to capture videos of structures, and machine vision approaches were developed to extract structural vibration from captured videos. This forward-looking review discusses the robot-based hardware, the machine vision approaches, and the capabilities of automated measurement of structural vibration. Emphases are placed on the clarifying the capabilities, technical limitations, promising solutions, and practical performance of robot-based approaches in field measurements that involve low vibration amplitudes and long-term monitoring requirements. Challenges and opportunities are discussed, and recommendations are provided to facilitate practical applications. This review will encourage the development and applications of robots for automated condition assessment of civil engineering structures. [ABSTRACT FROM AUTHOR]

Measuring distance is essential for health monitoring and condition assessment of civil engineering structures. This paper reviews recent advances in remote sensing technologies for measuring distance based on electromagnetic waves. Specifically, four families of technologies are reviewed, which are the Global Navigation Satellite Systems, microwave radars, laser-based methods, and vision-based methods. The reviewed content covers the measurement principles, signal processing methods, state-of-the-art applications, and key performance metrics. The investigated performance includes the measurement accuracy, sampling frequency, operating distance, robustness to the environment, and compatibility with autonomous platforms. Existing inconsistent viewpoints concerning the performance are discussed. Based on the features of different technologies, a decision tree is presented to facilitate selection of appropriate methods for intended applications. This research is expected to promote development and applications of remote sensing technologies for facilitating condition assessment of engineering structures. [ABSTRACT FROM AUTHOR]

Optical fibers with protective coatings have been used as distributed strain sensors for automated inspection in the construction, operation, and maintenance of various engineering structures. The presence of the protective coatings causes strain transfer effect, which can change the strain measurement of the distributed sensors. This study quantitatively evaluates the strain transfer for distributed fiber optic sensors subjected to an arbitrary strain field for the first time. Theoretical studies are performed to derive closed-form solutions for describing the strain transfer, and high-resolution (sub-millimeter) strain distributions were measured to validate the theoretical study. This study demonstrates that the strain transfer effect is dependent on the strain field in the host matrix, and the derived formulae enable correct interpretation of the strain measurement from the distributed sensor. This study provides theoretical foundations for using distributed fiber optic sensors to accurately measure strain distributions in engineering structures. Image 1 • The strain transfer in an optical fiber under an arbitrary strain field of host matrix is studied. • It is the first time to discover that the strain transfer ratio in distributed sensor can exceed 1. • The proposed method is validated using high-resolution measurement of strain distributions. • The study provides theoretical foundation for using distributed fiber optic strain sensors. [ABSTRACT FROM AUTHOR]

Current construction of engineering structures faces a variety of challenges, such as shortage of skilled labor, low adaptation to new technologies, and increasing concerns for negative impacts on the environment. These challenges have significantly compromised the productivity and economic benefits of the construction industry. Further, construction activities have been linked to significant emissions, particularly CO 2 associated with global warming. This study proposes a new paradigm of construction – Lego-inspired construction, and investigates the feasibility. Inspired by Lego blocks that can be assembled via dry joints and disassembled for different objects, this study presents blocks made using a bendable concrete, aiming to assemble various structures with dry joints. The blocks are used to assemble a prototype footbridge to demonstrate the assembling process, which is compatible with robotic systems for construction automation. The footbridge demonstrated reasonable load-carrying capability in mechanical testing. After the testing, the footbridge was disassembled, and the blocks were reused to assemble a frame for buildings. The proposed Lego-inspired construction is promising to greatly improve the construction efficiency and reduce the adverse impacts on the natural environment caused by conventional construction approaches. • This study proposes a new paradigm of construction – Lego-inspired construction. • Bendable concrete blocks are designed to assemble structures via dry joints. • A prototype footbridge is assembled to demonstrate the Lego-inspired construction. • The footbridge shows a reasonable load-carrying capability. • The footbridge is disassembled to reuse the blocks for a frame for buildings. [ABSTRACT FROM AUTHOR]

Fiber optic sensors represent an innovative technology for automated measurement of cable forces which are critical in construction and operation of many civil engineering structures. This paper reviews the fiber optic sensors that have been developed and applied to measure cable forces, including fiber Bragg grating, interferometer, and fully distributed sensors. The reviewed contents of each sensor technology include the sensing principle, sensor design, deployment on cables, data analytics, and sensing performance. The interactive behaviors between the sensor and the cable are discussed regarding the impacts on the measurement performance and mechanical properties of the cable, considering the sensor package and deployment methods. The performance and technology readiness levels of the different fiber optic sensors are compared. A guide is provided to select appropriate sensors for specific applications. This review is expected to promote further development and applications of fiber optic sensors for intelligent measurement of cable forces. [Display omitted] • Measurement of cable forces by using point and distributed fiber optic sensors is reviewed. • Fiber optic sensors measure the cable force along cable length in construction and operation. • Different types of fiber optic sensors and deployment methods are compared and discussed. • Technology readiness levels and selection of appropriate sensors are discussed. • Current challenges and future opportunities of fiber optic sensor technologies are reviewed. [ABSTRACT FROM AUTHOR]

• Distributed fiber optic sensors are used to detect buckling for thin-walled structures. • An effective and practical method is developed for shape reconstruction using strain fields. • Strain fields due to buckling are measured by fully distributed sensors with sub-mm resolution. • The proposed shape reconstruction method is compared with a computer-vision method. • Effects of spatial resolution and sensor deployment on shape reconstruction are discussed. This paper proposes to detect buckling and reconstruct three-dimensional deformations using distributed fiber optic sensors. The distributed sensors measured strain distributions with sub-millimeter resolutions based on optical frequency domain reflectometry in real time. Buckling was detected from high-resolution strain distributions measured from specimens. An effective and practical shape reconstruction approach was developed to derive nonsymmetrical deformations based on the strain distributions. The reconstructed shape was validated using a computer vision method that measured point cloud from the specimens. A parametric study was conducted to investigate the effects of the key sensing parameters such as the spatial resolution and sensor deployment scheme on the performance of the shape reconstruction approach, and used to optimize the resolution and deployment of distributed sensors. This research will advance the capability of buckling detection and shape reconstruction through distributed sensing for engineering structures under complex loading conditions. [ABSTRACT FROM AUTHOR]

This paper presents an intelligent photo interpretation approach to automatically monitor and characterize dense interconnected microcracks in strain-hardening cementitious composite (SHCC) featuring unique crack patterns in terms of crack number and crack width. The presented approach employs a stereo vision system that integrates binocular and monocular cameras for automatic detection, ranging, and quantification of cracks as well as characterization of crack patterns. The presented approach was implemented into evaluation of SHCC in flexural tests and direct tension tests. Dense microcracks were detected and ranged by the stereo vision system, segmented by an encoder-decoder approach, and quantified by an efficient computer vision approach. Evolution of the cracks was traced throughout the loading process until failure, and a statistical analysis revealed that the crack width was retained while the crack number monotonically increased. The interpretation time was shorter than 0.4 s for each photo, making the approach promising for monitoring of SHCC. The proposed system can be deployed for automated assessment of cementitious composites with complex crack patterns in material research and engineering structures. • Complex cracks in strain-hardening cementitious composites are intelligently characterized. • Ranging is incorporated to enable automatic photo interpretation without human intervention. • Real-time assessment of crack width and crack number is achieved. • The proposed approach showed high accuracy in crack detection and quantification. [ABSTRACT FROM AUTHOR]

Reconfigurable modular structures are able to be assembled using prefabricated modules and reconfigured to promote automated construction and to improve sustainability and resilience of infrastructure, while the computer-aided design and modeling of the modules are unclear. This study develops a many-objective optimization approach to design the modules made using strain-hardening cementitious composite. The proposed approach integrates a sequential surrogate model, Latin hypercube sampling method, Unified Non-dominated Sorting Genetic Algorithm III, and Technique for Order of Preference by Similarity to Ideal Solution to predict and optimize the properties of assemblages of the modules. Four objective functions were defined using the load-carrying capacity, deformability, stiffness, and volume. Results showed that the proposed method had reasonable prediction accuracy. The optimal design increased the load-carrying capacity, deformability, and stiffness by 22.8%, 11.5%, and 129.2%, respectively, and reduced the volume by 51.6%. This study is expected to effectively improve the design of reconfigurable modular structures. • Innovative concrete modules are designed and optimized for reconfigurable modular structures. • An engineer-friendly framework is presented for many-objective optimization of the modules. • The load capacity, stiffness, deflection, and volume of the modules are optimized. • A sequential surrogate model method is developed to predict the mechanical behaviors. • The UNSGA-III algorithm is utilized for many-objective optimization of the modules. [ABSTRACT FROM AUTHOR]

Measuring distance is critical for safety and quality in construction and operation of engineering structures. This paper proposes a framework to utilize cost-effective and robust ultra-wideband radio technology for wireless sensing of distance, presents a machine learning method based on extreme gradient boosting decision tree, and incorporates error mitigation methods to improve the measurement accuracy. In-situ measurement of distance for a highway bridge in operation was conducted to evaluate the performance of the proposed methods which demonstrated a sub-millimeter accuracy of distance measurement. The proposed methods show desired accuracy, cost-effectiveness, and robustness to the environment, and reveal a tradeoff between the accuracy and frequency of distance measurement. The tradeoff can be used to optimize the sensing system and signal processing program to satisfy the requirements in different applications. This study is expected to advance the capability of measuring distance in various automation processes for construction and operation of engineering structures. • A cost-effective wireless distance measurement method is presented using ultra-wideband radio. • A machine learning technique is used to increase the measurement accuracy to sub-mm level. • Tradeoff between sampling frequency and accuracy can be used to reconfigure the system. • Laboratory and field tests are used to evaluate the performance of the proposed method. [ABSTRACT FROM AUTHOR]