Nowadays, low-cost accelerometers are getting more attention from civil engineers to make Structural Health Monitoring (SHM) applications affordable and applicable to a broader range of structures. The present accelerometers based on Arduino or Raspberry Pi technologies in the literature share some of the following drawbacks: (1) high Noise Density (ND), (2) low sampling frequency, (3) not having the Internet’s timestamp with microsecond resolution, (4) not being used in experimental eigenfrequency analysis of a flexible and a less-flexible bridge, and (5) synchronization issues. To solve these problems, a new low-cost triaxial accelerometer based on Arduino technology is presented in this work (Low-cost Adaptable Reliable Accelerometer—LARA). Laboratory test results show that LARA has a ND of 51 µg/√Hz, and a frequency sampling speed of 333 Hz. In addition, LARA has been applied to the eigenfrequency analysis of a short-span footbridge and its results are compared with those of a high-precision commercial sensor.

In recent years, many scholars have dedicated their research to the development of low-cost sensors for monitoring of various parameters. Despite their high number of applications, the state of the art related to low-cost sensors in building monitoring has not been addressed. To fill this gap, this article presents a systematic review, following well-established methodology, to analyze the state of the art in two aspects of structural and indoor parameters of buildings, in the SCOPUS database. This analysis allows to illustrate the potential uses of low-cost sensors in the building sector and addresses the scholars the preferred communication protocols and the most common microcontrollers for installation of low-cost monitoring systems. In addition, special attention is paid to describe different areas of the two mentioned fields of building monitoring and the most crucial parameters to be monitored in buildings. Finally, the deficiencies in line with limited number of studies carried out in various fields of building monitoring are overviewed and a series of parameters that ought to be studied in the future are proposed.

The Building Information Modeling (BIM) methodology improves architectural and infrastructure projects by digitizing their processes throughout their life cycle stages, such as design, construction, management, monitoring, and operation. In recent years, the automation of these processes has been favored by the use of visual programming (VP) tools that have replaced conventional programming languages for visual schemes. The use of these tools in architectural projects is becoming increasing popular. However, this is not the case in infrastructure projects, for which the use of VP algorithms remains scarce. The aim of this work is to encourage both scholars and engineers to implement VP tools in infrastructure projects. For this purpose, this work reviews, for the first time in the literature, the state-of-the-art and future research trends of VP tools in infrastructure projects.

Arduino-based accelerometers are receiving wide attention from researchers to make long-term Structural Health Monitoring (SHM) feasible for structures with a low SHM budget. The current low-cost solutions found in the literature share some of the following drawbacks: (1) high noise density, (2) lack of wireless synchronization, (3) lack of automatic data acquisition and data management, and (4) lack of dedicated field tests aiming to compare mode shapes from Operational Modal Analysis (OMA) with those of a digital model. To solve these problems, a recently built short-span footbridge in Barcelona is instrumented using four Low-cost Adaptable Reliable Accelerometers (LARA). In this study, the automatization of the data acquisition and management of these low-cost solutions is studied for the first time in the literature. In addition, a digital model of the bridge under study is generated in SAP2000 using the available drawings and reported characteristics of its materials. The OMA of the bridge is calculated using Frequency Domain Decomposition (FDD) and Covariance Stochastic Subspace Identification (SSI-cov) methods. Using the Modal Assurance Criterion (MAC), the mode shapes of OMA are compared with those of the digital model. Finally, the acquired eigenfrequencies of the bridge obtained with a high-precision commercial sensor (HI-INC) showed a good agreement with those obtained with LARA.

The construction industry is increasingly adopting artificial intelligence (AI) to enhance productivity and safety, with object detection in visual data serving as a vital tool. However, developing robust object detection models demands extensive, high-quality datasets, which are often difficult to generate and maintain in construction due to the dynamic and complex nature of job sites. This paper presents an innovative approach to automating dataset generation using robotic process automation (RPA) and generative AI techniques, specifically, DALL-E 2. This approach not only accelerates dataset creation but also improves model performance by delivering balanced, high-quality inputs. To validate the proposed methodology, a case study of a building construction site is conducted. In this study, three commonly used convolutional neural network architectures—RetinaNet, Faster R-CNN, and YOLOv5—are trained with the artificially generated dataset to automate the identification of formworks and rebars during construction.