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The deformation behaviour of the asphalt concrete (AC) depends on the loading state and the temperature, accompanied by a large number of microcracks. In this study, the deformation and failure behaviour of the AC under uniaxial compression were investigated by the acoustic emission (AE). Both the stress–strain relationship and the energy dissipation were employed to analyse the macroscopic deformation of the specimen at low and medium temperatures. The AE parameters including count, energy and amplitude were measured in real-time during the loading process. The damage morphology of specimens was characterised by the failure location with the AE technology. Results show that with the rise of the temperature, the strength of AC specimens gradually decreased, which leads to a lower capacity in storing strain energy. The dissipative energy is greatly affected by the damage degree of the specimen. Both the event count and amplitude increased rapidly when the specimen failure occurred. The AE parameters are relatively sensitive to the deformation failure of asphalt mixtures influenced by the temperature. Furthermore, the results also imply that the AE method used in the present study to characterise the deformation failure in pavement engineering is more scientific and precise. [ABSTRACT FROM AUTHOR]

The Monte Carlo simulation (MCS) has been developed to estimate the strength distributions of heterogeneous materials influenced by different factors. The method presents a statistical correlation between the macroscopic properties and the non-uniform characteristics at meso scale. This study proposes an extensive MCS to investigate the influence of the aggregate morphology on deformation behaviour of unbound granular materials (UGMs) based on a real aggregate library and the discrete element method. Two indexes, elongation index (EI) and flatness index (FI), were used to evaluate the morphological characteristics of 3D aggregates rebuilt by the X-ray CT technique. Furthermore, repeated load triaxial tests of UGMs were simulated by 50 MCS samples. The effects of shapes and sizes of aggregates on deformation behaviours were statistically analysed. The results show that the deformation behaviour is dependent on the size and the EI distribution, but insensitive to the loading frequencies and FI distribution. [ABSTRACT FROM AUTHOR]

In order to build an appropriate prediction model of pavement friction coefficient attenuation, the effects of aggregate texture, on a basis of texture scanning and pavement testing of the dynamic friction coefficient, traffic volume, and rock characteristics on pavement slide decay resistance were studied, and the accuracy of the developed prediction model of friction coefficient was thereafter compared and analysed. First, based on the tire-pavement dynamic friction analyzer independently developed by the research group, accelerated loading tests under different traffic conditions were conducted by the orthogonal test method, and the dynamic friction coefficient of pavement during the abrasion process was recorded. At the same time, the non-contact laser profile measurement system was used to collect three-dimensional micro-texture data of coarse aggregate surface at different abrasion stages, and characteristic parameters such as height, wavelength, and shape were calculated. Second, the factors such as traffic volume and rock characteristics were incorporated, and the algorithms such as multiple linear stepwise regression, feedforward neural network, and random forest regression were adopted to develop estimation models of pavement friction. The results showed that the model constructed based on only texture feature parameters poorly represented pavement skid resistance. After introducing the two indicators of total traffic volume and rock type, the correlation coefficients of the model significantly improved, reaching 0.63, 0.58, and 0.82 respectively, indicating that the latter two factors have significant effects on the anti-skid attenuation of pavement. Moreover, the above three models can achieve satisfactory prediction results. In addition, when a large sample size can be obtained, a random forest algorithm is recommended to acquire higher prediction accuracy of pavement friction coefficient attenuation. [ABSTRACT FROM AUTHOR]

Construction accidents can lead to serious consequences. To reduce the occurrence of such accidents and strengthen the execution capabilities in on-site safety management, managers must analyze accident report texts in depth and extract valuable information from them. However, accident report texts are usually presented in unstructured or semi-structured forms; analyzing these texts manually requires a lot of time and effort, it is difficult to cope with the demand of analyzing a large number of accident texts, and the quality of key information extracted manually may be poor. Therefore, this study proposes a classification method based on natural language processing (NLP) technology. First, we developed a text classification model based on a convolutional neural network (CNN) that can automatically classify accident categories based on accident text features. Next, taking the classified fall accidents as an example, we extracted key information from accident narratives using the term frequency-inverse document frequency (TF-IDF) method and presented it visually using word clouds. The results show that the overall accuracy of the CNN model reaches 84%, which is better than the other three shallow machine-learning models. Then, eight key accident areas and three accident-prone operations were identified using the TF-IDF algorithm. This study can provide important guidance for project managers and can be used for on-site safety management to help prevent production safety accidents. [ABSTRACT FROM AUTHOR]

Mountainous expressways with bridge-tunnel groups are characterized by complex environments and high driving risks, making them crucial sections for highway safety. This study applied eye-tracking techniques to evaluate driving safety and comfort in bridge-tunnel groups. Drivers' pupil diameter and fixation point distribution were measured in real vehicle tests. The influence of tunnel length, adjacent tunnel spacing, and natural lighting on drivers' pupil diameters were compared and analyzed. The maximum transient velocity of pupil area was introduced to describe the drivers' visual load and driving comfort. The results indicate that the driving workload reaches its maximum in the first tunnel in bridge-tunnel groups and is positively correlated with the tunnel length in other sections. Excessive or insufficient distance between adjacent tunnels is detrimental to driving comfort. The driving workload is higher at night compared to during the day. Moreover, the greater tunnel length in bridge-tunnel groups and the larger number of tunnels, suggest a higher driving workload for drivers. Above all, strengthening the design and management of bridge-tunnel groups in mountainous expressways is necessary. [ABSTRACT FROM AUTHOR]