Damage-sensitive and domain-invariant feature extraction for vehicle-vibration-based bridge health monitoring

We introduce a physics-guided signal processing approach to extract a damage-sensitive and domain-invariant (DS & DI) feature from acceleration response data of a vehicle traveling over a bridge to assess bridge health. Motivated by indirect sensing methods' benefits, such as low-cost and low-maintenance, vehicle-vibration-based bridge health monitoring has been studied to efficiently monitor bridges in real-time. Yet applying this approach is challenging because 1) physics-based features extracted manually are generally not damage-sensitive, and 2) features from machine learning techniques are often not applicable to different bridges. Thus, we formulate a vehicle bridge interaction system model and find a physics-guided DS & DI feature, which can be extracted using the synchrosqueezed wavelet transform representing non-stationary signals as intrinsic-mode-type components. We validate the effectiveness of the proposed feature with simulated experiments. Compared to conventional time- and frequency-domain features, our feature provides the best damage quantification and localization results across different bridges in five of six experiments.Comment: To appear in Proc. ICASSP2020, May 04-08, 2020, Barcelona, Spain. IEE

An advanced binary slime mould algorithm for feature subset selection in structural health monitoring data

The 2022 Civil Engineering Research in Ireland (CERI) and Irish Transportation Research Network (ITRN) Conference, Dublin, Ireland, 25-26th August 2022Feature selection (FS) is an important task for data analysis, pattern classification systems, and data mining applications. In this paper, an advanced version of binary slime mould algorithm (ABSMA) is introduced for feature subset selection to enhance the capability of the original slime mould algorithm (SMA) for processing of measured data collected from monitoring sensors installed on structures. In the first step, structural response signals under ambient vibration are pre-processed according to statistical characteristics for feature extraction. In the second step, extracted features of a structure are reduced using an optimization algorithm to find a minimal subset of salient features by removing noisy, irrelevant and redundant data. Finally, the optimized feature vectors are used as inputs to the surrogate models based on radial basis function neural network (RBFNN). A benchmark dataset of a wooden bridge model is considered as a test example. The results indicate that the proposed ABSMA shows better performance and convergence rate in comparison with four well-known metaheuristic optimizations. Furthermore, it can be concluded that the proposed feature subset selection method has the capability of more than 80% data reduction.Science Foundation Irelan

Optimising existing digital workflow for structural engineering organisations through the partnering of BIM and Lean processes

Building Information Modelling (BIM) is now seen as one of the leading transformative processes within the Architectural, Engineering and Construction (AEC) sector and has the potential to assist in streamlining the structural design process. However, its practical implementation can often add another layer to the existing workflow and can result, to its detriment, in the primary objective of optimising structural workflows being hindered. This can lead to structural organisations producing 3D models in tandem with traditional drawings, a lack of human intervention regarding software interoperability, and a reluctance to move away from conventional work methods. This paper will explore how a lean approach to BIM adoption can optimise the digital structural workflow, thereby enhancing BIM adoption. Although much research has been conducted on BIM as an enabler of Lean, there remains a gap regarding the synergies in how Lean tools can advance BIM adoption within the structural discipline. The closing of this knowledge gap will be advanced by comparing existing digital workflows within a structural organisation against a proposed integrated BIM workflow underpinned through Lean. The findings highlight that while BIM and Lean offer enhanced digital solutions to modernise structural design office workflows, the true capability of these tools will not be realised without a cultural change

ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects

This paper presents a conceptual framework for the optimization of environmental sustainability in engineering projects, both for products and industrial facilities or processes. The main objective of this work is to propose a conceptual framework to help researchers to approach optimization under the criteria of sustainability of engineering projects, making use of current Machine Learning techniques. For the development of this conceptual framework, a bibliographic search has been carried out on the Web of Science. From the selected documents and through a hermeneutic procedure the texts have been analyzed and the conceptual framework has been carried out. A graphic representation pyramid shape is shown to clearly define the variables of the proposed conceptual framework and their relationships. The conceptual framework consists of 5 dimensions; its acronym is ADAPTS. In the base are: (1) the Application to which it is intended, (2) the available DAta, (3) the APproach under which it is operated, and (4) the machine learning Tool used. At the top of the pyramid, (5) the necessary Sensing. A study case is proposed to show its applicability. This work is part of a broader line of research, in terms of optimization under sustainability criteria.Telefónica Chair “Intelligence in Networks” of the University of Seville (Spain

A Machine Learning Approach to Bridge-Damage Detection Using Responses Measured on a Passing Vehicle

This paper proposes a new two-stage machine learning approach for bridge damage detection using the responses measured on a passing vehicle. In the first stage, an artificial neural network (ANN) is trained using the vehicle responses measured from multiple passes (training data set) over a healthy bridge. The vehicle acceleration or Discrete Fourier Transform (DFT) spectrum of the acceleration is used. The vehicle response is predicted from its speed for multiple passes (monitoring data set) over the bridge. Root-mean-square error is used to calculate the prediction error, which indicates the differences between the predicted and measured responses for each passage. In the second stage of the proposed method, a damage indicator is defined using a Gaussian process that detects the changes in the distribution of the prediction errors. It is suggested that if the bridge condition is healthy, the distribution of the prediction errors will remain low. A recognizable change in the distribution might indicate a damage in the bridge. The performance of the proposed approach was evaluated using numerical case studies of vehicle–bridge interaction. It was demonstrated that the approach could successfully detect the damage in the presence of road roughness profile and measurement noise, even for low damage levels