Data fusion approaches and applications for construction engineering

Data fusion can be defined as the process of combining data or information for estimating the state of an entity. Data fusion is a multidisciplinary field that has several benefits, such as enhancing the confidence, improving reliability, and reducing ambiguity of measurements for estimating the state of entities in engineering systems. It can also enhance completeness of fused data that may be required for estimating the state of engineering systems. Data fusion has been applied to different fields, such as robotics, automation, and intelligent systems. This paper reviews some examples of recent applications of data fusion in civil engineering and presents some of the potential benefits of using data fusion in civil engineering.

Visual pattern recognition models for remote sensing of civil infrastructure

As-built models have been proven useful in many project-related applications, such as progress monitoring and quality control. However, they are not widely produced in most projects because a lot of effort is still necessary to manually convert remote sensing data from photogrammetry or laser scanning to an as-built model. In order to automate the generation of as-built models, the first and fundamental step is to automatically recognize infrastructure-related elements from the remote sensing data. This paper outlines a framework for creating visual pattern recognition models that can automate the recognition of infrastructure-related elements based on their visual features. The framework starts with identifying the visual characteristics of infrastructure element types and numerically representing them using image analysis tools. The derived representations, along with their relative topology, are then used to form element visual pattern recognition (VPR) models. So far, the VPR models of four infrastructure-related elements have been created using the framework. The high recognition performance of these models validates the effectiveness of the framework in recognizing infrastructure-related elements.

Comparison of Civil Infrastructure Optical-based Spatial Data Acquisition Techniques

Infrastructure spatial data, such as the orientation and the location of in place structures and these structures' boundaries and areas, play a very important role for many civil infrastructure development and rehabilitation applications, such as defect detection, site planning, on-site safety assistance and others. In order to acquire these data, a number of modern optical-based spatial data acquisition techniques can be used. These techniques are based on stereo vision, optics, time of flight, etc., and have distinct characteristics, benefits and limitations. The main purpose of this paper is to compare these infrastructure optical-based spatial data acquisition techniques based on civil infrastructure application requirements. In order to achieve this goal, the benefits and limitations of these techniques were identified. Subsequently, these techniques were compared according to applications' requirements, such as spatial accuracy, the automation of acquisition, the portability of devices and others. With the help of this comparison, unique characteristics of these techniques were identified so that practitioners will be able to select an appropriate technique for their own applications.

Comparison of Civil Infrastructure Optical-based Spatial Data Acquisition Techniques

Infrastructure spatial data, such as the orientation and the location of in place structures and these structures' boundaries and areas, play a very important role for many civil infrastructure development and rehabilitation applications, such as defect detection, site planning, on-site safety assistance and others. In order to acquire these data, a number of modern optical-based spatial data acquisition techniques can be used. These techniques are based on stereo vision, optics, time of flight, etc., and have distinct characteristics, benefits and limitations. The main purpose of this paper is to compare these infrastructure optical-based spatial data acquisition techniques based on civil infrastructure application requirements. In order to achieve this goal, the benefits and limitations of these techniques were identified. Subsequently, these techniques were compared according to applications' requirements, such as spatial accuracy, the automation of acquisition, the portability of devices and others. With the help of this comparison, unique characteristics of these techniques were identified so that practitioners will be able to select an appropriate technique for their own applications.