Pavement Condition Index (PCI) is an important index to establish a proper maintenance and rehabilitation strategy of a road network. The index is calculated based on the present state of surface defects, deformation and cracking. The information is normally obtained by visual inspection and observation of road networks. Nowadays, various sensor-based visual inspection techniques are applied to obtain detailed information of a road network, and to automate the entire process of calculating PCI. Hyperspectral analysis is a technique to identify the spectral signature of a material in the electromagnetic spectrum. The technique is being applied to pavement condition evaluation. Some researchers have reported that Exposed Aggregate Index (EAI) has a relationship with the reflectance of a hyperspectral image of a road network. In this study, the possibility of using hyperspectral images for pavement condition evaluation is experimentally investigated and the relationship between EAI and PCI is addressed.

The global trends of shorter delivery times and the safety of important payload in production networks are leading to higher synchronization efforts between production and delivery processes. By now, research activities in intelligent shipment are expanding quickly in the case of possibilities and importance of usage, which means payload that can identify, monitor or locate itself. In this study, it is proposed that new generation system for continuously monitoring payloads during delivery; real-time monitoring of truck loading states; a new improved algorithm for intelligent monitoring of delivery processing; the possibility of a detailed analysis of the truck loading states in real-time and payload safety; and more efficient truck tracking.

The widespread sensors in a structural monitoring system provide vital support to its operation. Data is obtainedf rom sensors in a structural health monitoring system for integrity assessment of the structure, and false alarm will be frequently triggered if a faulty sensor is detected. In this study, a proposed method based on machine learning algorithm and Gaussian distribution is present to identify sensor fault.

The 4th industrial revolution is promoting the development of efficient maintenance technology through fusion with advanced IT technology. Digital Twin technology, an advanced fusion IT technology, is widely used in the fields of plant facilities and processes, performance monitoring of wind turbines and engine. In this study, the application of digital twin technology to large civil engineering structures, especially bridges, was presented.

Construction safety is one of the significant problems on the world. Deep learning is an emerging term that acquires, processes and analyses image or video data to help computers have a high-level visual understanding of the world. In recent years, it has been introduced into the construction industry for improvements of occupational health and safety. This research contributes in solving this problem by using deep learning only RGB images that output detects the hazard zone on construction sites. The main goal of this study is to use different computer vision and deep learning to develop for different cases concerning fall related hazards.

Recently, due to advancement in construction techniques, structures are being constructed much faster than before. Therefore, structures such as cable stayed bridges and suspension bridges, must be inspected regularly to assure their main elements (i.e., load carrying cables) are healthy and sound. Structures can be tested using conventional nondestructive testing methods such as magnetic flux leakage (MFL), eddy current testing, acoustic emission and etc. In this study, it was tried to detect cross sectional reduction in steel rod using a time dependent numerical simulation of coil sensor based on MFL principle.

Ground penetrating radar (GPR) is a typical sensor system for underground objects detection area. The multichannel GPR devices can give more detail and informative three-dimensional (3D) data for classification underground objects. Spatial information of underground objects can be well characterized in the three-dimensional GPR block data which consists of several B-scan and C-scan data. In this article underground object classification method is proposed by using 3D GRP data. Deep learning technique is recently being adopted into this field due to its powerful image classification capacity. The 3D GRP block data is then used to train deep three-dimensional convolution neural network (3D CNN). The proposed method successfully classifies cavity, pipe, manhole and subsoils having small false positive errors. The suggested method is experimentally validated by area data collected on urban roads in Seoul, South Korea.

Pavement condition deteriorates due to various environmental issues. This can be seen on the pavement surface as a form of distress. A crack can be considered as a typical form of pavement distress in which it may reveal a critical condition of the road. Therefore, automatic and accurate detection of pavement crack and segmentation are crucial for pavement condition assessment and maintenance.