High-strength concrete (HSC) is a functional material possessing superior mechanical performance and considerable durability, which has been widely used in long-span bridges and high-rise buildings. Unconfined compressive strength (UCS) is one of the most crucial parameters for evaluating HSC performance. Previously, the mix design of HSC is based on the laboratory test results which is time and money consuming. Nowadays, the UCS can be predicted based on the existing database to guide the mix design with the development of machine learning (ML) such as back-propagation neural network (BPNN). However, the BPNN’s hyperparameters (the number of hidden layers, the number of neurons in each layer), which is commonly adjusted by the traditional trial and error method, usually influence the prediction accuracy. Therefore, in this study, BPNN is utilised to predict the UCS of HSC with the hyperparameters tuned by a bio-inspired beetle antennae search (BAS) algorithm. The database is established based on the results of 324 HSC samples from previous literature. The established BAS-BPNN model possesses excellent prediction reliability and accuracy as shown in the high correlation coefficient (R = 0.9893) and low Root-mean-square error (RMSE = 1.5158 MPa). By introducing the BAS algorithm, the prediction process can be totally automatical since the optimal hyperparameters of BPNN are obtained automatically. The established BPNN model has the benefit of being applied in practice to support the HSC mix design. In addition, sensitivity analysis is conducted to investigate the significance of input variables. Cement content is proved to influence the UCS most significantly while superplasticizer content has the least significance. However, owing to the dataset limitation and limited performance of ML models which affect the UCS prediction accuracy, further data collection and model update must be implemented.

In recent years, interest in BIM and GIS applications in civil engineering has been growing. For bridge engineering, BIM/GIS applications such as simulation, visualization, and secondary development have been used to assist practitioners in managing bridge construction and decision-making, including selection of bridge location maintenance decisions. In situ 3D modelling of existing bridges with detailed images from UAV camera has allowed engineers to conduct remote condition assessments of bridges and decide on required maintenance actions. Several studies have investigated the applications of BIM/GIS technology on bridge projects. However, there has been limited focus on reviewing the outcomes of these studies to identify the limitations of BIM and GIS applications on bridge projects. Therefore, the aim of this study was to review the research on BIM/GIS technology applications in bridge projects over the last decade. Using a systematic review process, a total of 90 publications that met the inclusion criteria were reviewed in this study. The review identified the state-of-the-art methods of BIM and GIS applications, respectively, at the planning and design, construction, and operation and maintenance phases of bridge projects. However, the findings point to segregated application of BIM and GIS at all phases of bridge projects. The findings of this study will contribute to guiding practitioners in selecting appropriate BIM and GIS technologies for different aspects of bridge projects.

Smart building is the result of the penetration of information technology and control technology into traditional buildings, and is the future development direction of buildings. User-centric building smart design can achieve a sustainable life, and smart technology integration based on smart design can support sustainable development and improve user satisfaction, happiness, and overall quality of life. In intelligent design, researchers focus on the “people-oriented” approach, designed to bring users the ultimate interactive experience. Based on the interactive experience principle of smart design in the smart building system, this article classifies and summarizes intelligent design from the “five senses” interaction, including visual interaction, voice interaction, tactile interaction, cognitive interaction, and emotional interaction. We analyze the application of smart design in architecture and discuss how to embody the principles of user-centered interactive experience design in the process of smart design. This article provides a comprehensive and systematic literature review, clarifies the importance of the “people-oriented” approach in the smart design of buildings, and summarizes how to improve the sustainability of intelligent buildings from the perspective of a “people-oriented” approach. This paper proposes future research suggestions and directions and promotes the sustainable development of the smart building.

Three-dimensional concrete printing is a promising technology and attracts the significant attention of research and industry. However, printable and mechanical capacities are required for 3D printable cementitious materials. Moreover, the quantitative analysis methods of printable performance are limited and have low sensitivity. In this study, the orthogonal experiment through samples combining 3D concrete printing method with fly ash, silica fume, and ground granulated blast furnace slag was designed to obtain the printable and mechanical property influence of various mix proportions. Furthermore, multiple industrial wastes were utilized to improve material sustainability. Meanwhile, the static and dynamic extrusion pressure measured by the original 3D printing extrudability tester were verified to achieve a high-sensitivity evaluating indicator. Thereby, a novel high-sensitivity quantitative analysis method of printable capacity was established to explore the influence of industrial wastes usage on the printability of 3D printable mortars. The optimum dosage of fly ash, silica fume, and ground granulated blast furnace slag was 20 wt.%, 15 wt.%, and 10 wt.%, respectively, based on printable and mechanical property experiments. Furthermore, the optimum dosage was employed to print the sample and achieved a higher compressive strength (56.3 MPa) than the control cast.

Three-dimensional (3D) concrete printing (3DCP) technology attracts significant attention from research and industry. Moreover, adequate mechanical performance is one of the primary properties for materials, meeting the demand of structural safety using 3DCP technology. However, research on curing conditions as the significant influence factor of mechanical capacity is required to accelerate the practical application of 3DCP technology. This study aims to explore the impact of various steam curing conditions (heating rate, constant temperature time, and constant temperature) on the mechanical performance of printed concrete containing solid wastes. Moreover, the optimal steam curing conditions are obtained for compressive, tensile, and flexural properties in different directions. Subsequently, anisotropies in the mechanical properties of printed composites and interlayer bonding behaviors are investigated when various curing conditions are employed. The result shows that steam curing conditions and solid waste incorporation improves the interlayer bond for 3D printed cement-based composites.

In recent years, wireless sensors have progressively supplanted conventional limited sensors owing to their attributes of small size, low cost, and high accuracy. Consequently, there has been a growing interest in leveraging wireless sensor networks for bridge structural health monitoring applications. By employing wireless sensor nodes to gather data from various segments of the bridge, information is relayed to a signal-receiving base station. Subsequently, the health status of the bridge is inferred through specific data processing and analysis, aiding monitoring personnel in making informed decisions. Nonetheless, there are limitations in this research, particularly pertaining to power consumption and efficiency issues in data acquisition and transmission, as well as in determining the appropriate wireless sensor types and deployment locations for different bridge configurations. This study aims to comprehensively examine research on the utilization of wireless sensor networks in the realm of bridge structural health monitoring. Employing a systematic evaluation methodology, more than one hundred relevant papers were assessed, leading to the identification of prevalent sensing techniques, data methodologies, and modal evaluation protocols in current use within the field. The findings indicate a heightened focus among contemporary scholars on challenges arising during the data acquisition and transmission processes, along with the development of optimal deployment strategies for wireless sensor networks. In continuing, the corresponding technical challenges are provided to address these concerns.