As the use of construction robots continues to increase, ensuring safety and productivity while working alongside human workers becomes crucial. To prevent collisions, robots must recognize human behavior in close proximity. However, single, or RGB-depth cameras have limitations, such as detection failure, sensor malfunction, occlusions, unconstrained lighting, and motion blur. Therefore, this study proposes a multiple-camera approach for human activity recognition during human–robot collaborative activities in construction. The proposed approach employs a particle filter, to estimate the 3D human pose by fusing 2D joint locations extracted from multiple cameras and applies long short-term memory network (LSTM) to recognize ten activities associated with human and robot collaboration tasks in construction. The study compared the performance of human activity recognition models using one, two, three, and four cameras. Results showed that using multiple cameras enhances recognition performance, providing a more accurate and reliable means of identifying and differentiating between various activities. The results of this study are expected to contribute to the advancement of human activity recognition and utilization in human–robot collaboration in construction.

With the increasing interest in mobile robots for construction applications, autonomous navigation in unstructured and uneven construction sites has become a critical challenge. To ensure the safe and robust navigation of a robot in such environments, this study develops an optimal obstacle-avoiding path planner for stable posture (OOPS) that minimizes the distance to the goal area and stabilizes the posture of the robot. In this study, a new algorithm was developed by significantly improving the quick-rapidly exploring random tree* (Q-RRT*) algorithm to find a feasible path and converge to the optimal path more quickly. A cost function was defined to stabilize the posture of the robot. A simulation experiment was conducted to examine the feasibility of the OOPS algorithm, and its performance was compared with those of other algorithms. The OOPS algorithm was also validated in an experiment in a real-world outdoor environment with sloped hills and several obstacles. The results demonstrate that the OOPS algorithm outperforms other algorithms in terms of the time needed to find the initial solution, time to convergence on the optimal solution, and rate of success in reaching the goal. A mobile robot with the OOPS algorithm is able to start navigating sooner, and the algorithm takes less time than other algorithms to produce paths that are closer to the optimal path. The robot’s posture is more stable when it follows the path obtained from the OOPS algorithm in both simulation and real-world tests. Therefore, the OOPS algorithm can be effectively used for applications in uneven, highly sloped, and unstructured outdoor environments.

In the construction industry, predicting business failure and providing early warnings are critical challenges in the prevention of business failure chain reactions. Most relevant studies have developed models that predicted the probability of business failure within 1 year using financial ratios. Although a few studies have attempted to use nonfinancial information, they did not provide empirical evidence that this addition can improve the prediction performance of a model. To address these problems, this study proposed a model that used not only accounting variables but also construction market and macroeconomic variables to predict failure probability from 1 to 3 years. We examined the effects of combinations of these variables on the business failure prediction performance of construction contractors in the United States and compared the effects of combinations of these variables between three models that predict business failure within 1, 2, and 3 years. This study developed a prediction model using a long short-term memory (LSTM) recurrent neural network (RNN), which is a deep-learning algorithm. The results showed that the prediction model using both the construction market and macroeconomic variables had approximately 2%, 3%, and 4% higher prediction performance compared with that using only accounting variables when predicting within 1, 2, and 3 years, respectively. This means that the business failure prediction model had superior prediction performance from a long-term perspective when the construction market and macroeconomic variables were used in addition to accounting variables. The results of this study are expected to provide empirical evidence regarding the effect of input variable selection on the prediction performance for each prediction period and useful references for improving performance of predicting business failure of construction contractors.

Human–robot collaboration (HRC) has emerged as a promising frontier within the construction industry, offering the potential to enhance productivity, safety, and efficiency. The effectivene...