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Prediction models are essential tools for transportation agencies to forecast the condition of bridge decks based on available data, and artificial intelligence is paramount for this purpose. This study aims at proposing a bridge deck condition prediction model by assessing various classification and regression algorithms.

The 2019 National Bridge Inventory database is considered for model development. Eight different feature selection techniques, along with their mean and frequency, are used to identify the critical features influencing deck condition ratings. Thereafter, four regression and four classification algorithms are applied to predict condition ratings based on the selected features, and their performances are evaluated and compared with respect to the mean absolute error (MAE).

Classification algorithms outperform regression algorithms in predicting deck condition ratings. Due to its minimal MAE (0.369), the random forest classifier with eleven features is recommended as the preferred condition prediction model. The identified dominant features are superstructure condition, age, structural evaluation, substructure condition, inventory rating, maximum span length, deck area, average daily traffic, operating rating, deck width, and the number of spans.

The proposed bridge deck condition prediction model offers a valuable tool for transportation agencies to plan maintenance and resource allocation efficiently, ultimately improving bridge safety and serviceability.

This study provides a detailed framework for applying machine learning in bridge condition prediction that applies to any bridge inventory database. Moreover, it uses a comprehensive dataset encompassing an entire region, broadening the model’s applicability and representation.

Bridge deterioration is a critical risk to public safety, which mandates regular inspection and maintenance to ensure sustainable transport services. Many models have been developed to aid in understanding deterioration patterns and in planning maintenance actions and fund allocation. This study aims at developing a deep-learning model to predict the deterioration of concrete bridge decks.

Three long short-term memory (LSTM) models are formulated to predict the condition rating of bridge decks, namely vanilla LSTM (vLSTM), stacked LSTM (sLSTM), and convolutional neural networks combined with LSTM (CNN-LSTM). The models are developed by utilising the National Bridge Inventory (NBI) datasets spanning from 2001 to 2019 to predict the deck condition ratings in 2021.

Results reveal that all three models have accuracies of 90% and above, with mean squared errors (MSE) between 0.81 and 0.103. Moreover, CNN-LSTM has the best performance, achieving an accuracy of 93%, coefficient of correlation of 0.91, R2 value of 0.83, and MSE of 0.081.

The study used the NBI bridge inventory databases to develop the bridge deterioration models. Future studies can extend the model to other bridge databases and other applications in the construction industry.

This study provides a detailed and extensive data cleansing process to address the shortcomings in the NBI database. This research presents a framework for implementing artificial intelligence-based models to enhance maintenance planning and a guideline for utilising the NBI or other bridge inventory databases to develop accurate bridge deterioration models. Future studies can extend the model to other bridge databases and other applications in the construction industry.

The purpose of this paper is to examine the importance of contractual management (CM), process management (PM) and human management (HM) factors as critical success factors (CSFs) in Malaysian housing projects. Additionally, it delves into the moderating influence of knowledge sharing (KS) on the relationship between HM and project success.

This study used a survey-based instrument to collect data from a total of 133 G7 class contractors. The stratified sampling method was used for data collection. Subsequently, structural equation modeling with SmartPLS was used for model evaluation.

The findings of this study indicate that CM, PM and HM exhibit significant relationships with housing project success. Furthermore, the research reveals that KS acts as a moderator in the relationship between HM practices and the success of housing projects.

Although this study identified a significant relationship in explaining CSFs for housing project success in Malaysia, it only considers internal CSFs such as CM, PM and HM. It is suggested that future research incorporate external factors such as political support, national policy, currency stability and industry structure to provide a more comprehensive understanding of housing project success.

The results provide supportive evidence that CM, PM and HM are important CSFs in the success of housing projects. This finding is consistent with relational contractual theory, systems theory and social interaction theory. Moreover, the research underscores the nuanced impact of KS, serving as a moderating factor in the association between HM and project success. Consequently, these outcomes substantiate the applicability of the socialization, externalization, combination and internalization framework within the construction sector, particularly within the sphere of housing sector.