Machine learning-based prediction of the seismic bearing capacity of a shallow strip footing over a void in heterogeneous soils

The seismic bearing capacity of a shallow strip footing above a void displays a complex dependence on several characteristics, linked to geometric problems and to the soil properties. Hence, setting analytical models to estimate such bearing capacity is extremely challenging. In this work, machine learning (ML) techniques have been employed to predict the seismic bearing capacity of a shallow strip footing located over a single unsupported rectangular void in heterogeneous soil. A dataset consisting of 38,000 finite element limit analysis simulations has been created, and the mean value between the upper and lower bounds of the bearing capacity has been computed at the varying undrained shear strength and internal friction angle of the soil, horizontal earthquake accelerations, and position, shape, and size of the void. Three machine learning techniques have been adopted to learn the link between the aforementioned parameters and the bearing capacity: multilayer perceptron neural networks; a group method of data handling; and a combined adap-tive-network-based fuzzy inference system and particle swarm optimization. The performances of these ML techniques have been compared with each other, in terms of the following statistical performance indices: coefficient of determination (R2); root mean square error (RMSE); mean absolute percentage error; scatter index; and standard bias. Results have shown that all the ML techniques perform well, though the multilayer perceptron has a slightly superior accuracy featuring notewor-thy results (R2 = 0.9955 and RMSE = 0.0158)

An alarming system for dangerous proximity situations in construction sites based on workers and mobile equipment tracking

The construction industry and workplaces are known as one of the most adventurous and dangerous industries in the world, incurring more occupational fatalities than any other sectors. This has led researchers to pay greater attention to the use of new technologies, such as the Internet of Things (IoT) technology, to improve safety. The IoT is helping many businesses and consumers to be more efficient and effective in day-to-day operations and tasks. It would only be logical to assume that there must be a way that the IoT could do the same for high-risk industries such as the construction industry. In this study, using a smartphone and fingerprint technique on the Wi-Fi network and the Global Positioning System (GPS), a system has been introduced to detect people approaching dangerous places by tracking them and to provide the necessary warnings. The system consists of three software products the first of which is responsible for performing calculations on the cloud server. The second one is installed on the mobile phones of site managers to record the necessary information in the system database. The third software is installed on the mobile phones of workers and has the task of tracking them and receiving the system warnings. In addition, this system is working in indoors and outdoors areas and has other advantages such as ease of operation, low implementation cost, and higher accuracy than other similar systems. In this study, an average detection error of 1.2 meters has been achieved, which is an acceptable error and indicates the correct operation of the system. Using the proposed IoT system in a construction site’s safety management system will provide real-time data on critical safety functions. As a result, it also reduces the accidents of the construction site and the costs around incidents, injuries, and workers’ compensation