Abstracts – Browse Results

Search or browse again.

Click on the titles below to expand the information about each abstract.
Viewing 6 results ...

Chang, S-h (2013) Evaluating essential liabilities on design professionals under earthquake’s impact: Taiwan experience. Engineering, Construction and Architectural Management, 20(02), 181-94.

Hwang, B-G, Tan, H F and Sathish, S (2013) Capital project performance measurement and benchmarking in Singapore. Engineering, Construction and Architectural Management, 20(02), 143-59.

Lee, J, Blumenstein, M, Guan, H and Loo, Y-C (2013) Minimising uncertainty in long-term prediction of bridge element. Engineering, Construction and Architectural Management, 20(02), 127-42.

  • Type: Journal Article
  • Keywords: assets management; bridges; condition monitoring; modelling; neural nets; performance; prediction
  • ISBN/ISSN: 0969-9988
  • URL: https://doi.org/10.1108/09699981311303008
  • Abstract:
    Purpose – Successful bridge management system (BMS) development requires a reliable bridge deterioration model, which is the most crucial component in a BMS. Historical condition ratings obtained from biennial bridge inspections are a major source for predicting future bridge deterioration in BMSs. However, historical condition ratings are very limited in most bridge agencies, thus posing a major barrier for predicting reliable future bridge performance. The purpose of this paper is to present a preliminary study as part of a long-term research on the development of a reliable bridge deterioration model using advanced Artificial Intelligence (AI) techniques. Design/methodology/approach – This proposed study aims to develop a reliable deterioration model. The development work consists of two major Stages: stage 1 – generating unavailable bridge element condition rating records using the Backward Prediction Model (BPM). This helps to provide sufficient historical deterioration patterns for each element; and stage 2 – predicting long-term condition ratings based on the outcome of Stage 1 using time delay neural networks (TDNNs). Findings – Long-term prediction using proposed method can also be expressed in the same form of inspection records – element quantities of each bridge element can be predicted. The proposed AI-based deterioration model does not ignore critical failure risks in small number of bridge elements in low condition states (CSs). This implies that the risk in long-term predictions can be reduced. Originality/value – The proposed methodology aims to utilise limited bridge inspection records over a short period to predict large datasets spanning over a much longer time period for a reliable, accurate and efficient long-term bridge deterioration model. Typical uncertainty, due to the limitation of overall condition rating (OCR) method, can be minimised in long-term predictions using limited inspection records.

Li, H, Jin, Z, Li, V, Liu, G and Skitmore, R M (2013) An entry mode decision-making model for the international expansion of construction enterprises. Engineering, Construction and Architectural Management, 20(02), 160-80.

Shehab, T and Farooq, M (2013) Neural network cost estimating model for utility rehabilitation projects. Engineering, Construction and Architectural Management, 20(02), 118-26.

Zhang, L and Fan, W (2013) Improving performance of construction projects: A project manager's emotional intelligence approach. Engineering, Construction and Architectural Management, 20(02), 195-207.