Non-destructive testing (NDT) methods are important alternatives when destructive tests are not feasible to examine the in situ concrete properties without damaging the structure. The rebound hammer test and the ultrasonic pulse velocity test are two popular NDT methods to examine the properties of concrete. The rebound of the hammer depends on the hardness of the test specimen and ultrasonic pulse travelling speed is related to density, uniformity, and homogeneity of the specimen. Both of these two methods have been adopted to estimate the concrete compressive strength. Statistical analysis has been implemented to establish the relationship between hammer rebound values/ultrasonic pulse velocities and concrete compressive strength. However, the estimated results can be unreliable. As a result, this research proposes an Artificial Intelligence model using support vector machines (SVMs) for the estimation. Data from 95 cylinder concrete samples are collected to develop and validate the model. The results show that combined NDT methods (also known as SonReb method) yield better estimations than single NDT methods. The results also show that the SVM model is more accurate than the statistical regression model.

A construction site features an open field and complexity and relies mainly on manual labor for construction progress, quality, and field management to facilitate job site coordination and productive results. It has a tremendous impact on the effectiveness and efficiency of job site supervision. However, most job site workers take photos of the construction activities. These photos serve as aids for project management, including construction history records, quality, and schedule management. It often takes a great deal of time to process the many photos taken. Most of the time, the image data are processed passively and used only for reference, which could be better. For this, a construction activity image recognition system is proposed by incorporating image recognition through deep learning, using the powerful image extraction ability of a convolution neural network (CNN) for automatic extraction of contours, edge lines, and local features via filters, and feeding feature data to the network for training in a fully connected way. The system is effective in image recognition, which is in favor of telling minute differences. The parameters and structure of the neural network are adjusted for using a CNN. Objects like construction workers, machines, and materials are selected for a case study. A CNN is used to extract individual features for training, which improves recognizability and helps project managers make decisions regarding construction safety, job site configuration, progress control, and quality management, thus improving the efficiency of construction management.

In the construction industry, non–destructive testing (NDT) methods are often used in the field to inspect the compressive strength of concrete. NDT methods do not cause damage to the existing structure and are relatively economical. Two popular NDT methods are the rebound hammer (RH) test and the ultrasonic pulse velocity (UPV) test. One major drawback of the RH test and UPV test is that the concrete compressive strength estimations are not very accurate when comparing them to the results obtained from the destructive tests. To improve concrete strength estimation, the researchers applied artificial intelligence prediction models to explore the relationships between the input values (results from the two NDT tests) and the output values (concrete strength). In-situ NDT data from a total of 98 samples were collected in collaboration with a material testing laboratory and the Professional Civil Engineer Association. In-situ NDT data were used to develop and validate the prediction models (both traditional statistical models and AI models). The analysis results showed that AI prediction models provide more accurate estimations when compared to statistical regression models. The research results show significant improvement when AI techniques (ANNs, SVM and ANFIS) are applied to estimate concrete compressive strength in RH and UPV tests.

The construction industry faces persistent occupational health and safety challenges, with numerous risks arising from construction sites’ complex and dynamic nature. Accidents frequently result from inadequate safety distances and poorly managed work-er–machine interactions, highlighting the need for advanced safety management solutions. This study develops and validates an innovative hazard warning system that leverages deep learning-based image recognition (YOLOv7) and Internet of Things (IoT) modules to enhance construction site safety. The system achieves a mean average precision (mAP) of 0.922 and an F1 score of 0.88 at a 0.595 confidence threshold, detecting hazards in under 1 s. Integrating IoT-enabled smart wearable devices provides real-time monitoring, delivering instant hazard alerts and personalized safety warnings, even in areas with limited network connectivity. The system employs the DIKW knowledge management framework to extract, transform, and load (ETL) high-quality labeled data and optimize worker and machinery recognition. Robust feature extraction is performed using convolutional neural networks (CNNs) and a fully connected approach for neural network training. Key innovations, such as perspective projection coordinate transformation (PPCT) and the security assessment block module (SABM), further enhance hazard detection and warning generation accuracy and reliability. Validated through extensive on-site experiments, the system demonstrates significant advancements in real-time hazard detection, improving site safety, reducing accident rates, and increasing productivity. The integration of IoT enhances scalability and adaptability, laying the groundwork for future advancements in construction automation and safety management.

The geological condition of Ho Chi Minh (HCM) City is soft soil and high groundwater and includes two main structural layers such as Pleistocene and Holocene sediments. Therefore, deep excavation of all the high-rise buildings in the city is usually supported by concrete retaining walls such as the diaphragm or bored pile retaining walls. The system limits the excavation wall deflection during the construction process which could pose a potential risk to the construction and neighborhood areas. To estimate wall deformation at a highly accurate and efficient level, this study presents several machine learning models including feed-forward neural network back-propagation (FFNN-BP), long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), and support vector regression (SVR). The database for the experiment was obtained from a high building in HCM City, Vietnam. The database is deployed to implement the proposed algorithms in walk-forward validation technique. As a result, the Bi-LSTM model reduced prediction errors and improved robustness than the LSTM, FFNN-BP, and SVR models. Bi-LSTM, LSTM, and FFNN-PB could be used for predicting deep excavation wall deflection. In the meantime, not only could the estimated results support safety monitoring and early warning during the construction stages but also could contribute to legal guidelines for the architecture of deep excavations in the city’s soft ground.

Compressive strength is probably one the most crucial properties of concrete material. For existing structures, core samples are drilled and tested to obtain the concrete compressive strength. Many times, taking core samples is not feasible, and as a result, nondestructive methods to examine the concrete are required. The rebound hammer test is one of the most popular methods to estimate concrete compressive strength without causing damage to the existing structure. The test is inexpensive and can be easily conducted compared to other nondestructive testing methods. Also, concrete compressive strength estimations can be obtained almost instantly. However, previous results have shown that concrete compressive strength estimations obtained from rebound hammer tests are not very accurate. As a result, this research attempts to apply artificial intelligence prediction models to estimate concrete compressive strength using data from in situ rebound hammer tests. The results show that artificial intelligence methods can effectively improve in situ concrete compressive strength estimations in rebound hammer tests.