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This study investigated the strength behaviors of dredged clays treated with metakaolin (MK) and fly ash (FA). A series of laboratory tests including direct shear and unconfined compressive strength tests were performed on MK and FA-treated dredged clay at various MK contents and different curing times. The direct shear test results showed that the shear stress and displacement curves for all specimens tested are strain-softening types and influenced by MK content and curing time. The variation curve at a higher MK content or higher curing time is always higher than that at a lower one. The shear strength from the direct shear test at a given vertical stress consistently increases linearly with the increase of MK content while increasing parabolically with curing time. This indicates both curing time and MK content contribute to the development of the shear strength. Both strength parameters c and φ grow with increasing MK content and curing time. Unconfined compressive strength test results show the strain-softening behavior for all tested specimens. Compared with adding FA alone or MK alone, adding both fly ash and MK exhibited a better treatment effect in the unconfined compressive strength, which increases with increasing MK content and curing time. [ABSTRACT FROM AUTHOR]

AbstractThis study investigated the strength characteristics of dredged marine clays treated with fly ash with varying porewater salinities. A series of laboratory tests were conducted on fly ash-treated dredged marine clay specimens with various salinities and curing times. In the direct shear tests, observed patterns revealed a consistent initial rapid increase in shear stress with the increase of horizontal displacement, followed by either a gradual decline or stabilization after reaching the peak shear stress. The variation curve consistently positioned the lower salinity or longer curing time above the higher salinity or shorter curing time. Furthermore, the shear strength at a given vertical stress consistently decreases with increasing salinity while ascending with increasing curing time. This indicated that curing time positively influences shear strength development, while the presence of salt negatively affects it. Both strength parameters cohesion and friction angle exhibited growth trends with increased curing time but displayed a decline as salinity increased from 0% to 6%. Unconfined compressive strength test results showed a strain-softening behavior for all tested specimens. The compressive strength demonstrated a declining trend with rising salinity, with a more pronounced decrease observed within the initial 7d compared to the less significant change between 7d and 28d. [ABSTRACT FROM AUTHOR]

As globalization accelerates, the linguistic diversity and semantic complexity of in-vehicle communication is increasing. In order to meet the needs of different language speakers, this paper proposes an interactive attention-based contrastive learning framework (IABCL) for the field of in-vehicle dialogue, aiming to effectively enhance cross-lingual natural language understanding (NLU). The proposed framework aims to address the challenges of cross-lingual interaction in in-vehicle dialogue systems and provide an effective solution. IABCL is based on a contrastive learning and attention mechanism. First, contrastive learning is applied in the encoder stage. Positive and negative samples are used to allow the model to learn different linguistic expressions of similar meanings. Its main role is to improve the cross-lingual learning ability of the model. Second, the attention mechanism is applied in the decoder stage. By articulating slots and intents with each other, it allows the model to learn the relationship between the two, thus improving the ability of natural language understanding in languages of the same language family. In addition, this paper constructed a multilingual in-vehicle dialogue (MIvD) dataset for experimental evaluation to demonstrate the effectiveness and accuracy of the IABCL framework in cross-lingual dialogue. With the framework studied in this paper, IABCL improves by 2.42% in intent, 1.43% in slot, and 2.67% in overall when compared with the latest model. [ABSTRACT FROM AUTHOR]

This study presents an experimental investigation of the interface behaviors of recycled asphalt pavement (RAP) reinforced with biaxial geogrid. A series of direct shear tests were conducted on compacted RAP specimens with or without geogrid under different normal stresses (50, 75, and 100 kPa) and test temperatures (0, 20, and 35°C) using an improved temperature-controlled direct shear apparatus. The effect of test temperature on interface shear strength and strength parameters was systematically examined. Test results showed that shear stress versus shear displacement curves of RAP specimens with or without geogrid show strain-softening characteristics. Under the same normal stress, the curve of the reinforced RAP sample with a high temperature is always below that with a low temperature, which indicates the test temperature has an adverse effect on the strength of the reinforced RAP sample. The shear strength consistently increases linearly with the increase of applied normal stress for all RAP samples. The shear strength with geogrid is greater than pure RAP under a given test temperature and normal stress. The shear strength of RAP samples at lower test temperatures is higher than that at a higher temperature. Both apparent adhesion intercept of RAP-geogrid and cohesion of RAP show a decreasing trend with the increase of the test temperature and tend to be stable with increasing temperature. Both the interface friction angle of RAP-geogrid and the internal friction angle of RAP slightly decrease with the increase of test temperature. For RAP-geogrid, the value of the apparent adhesion intercept is higher than that for sand and gravel with geosynthetics. The interface friction angle is close to that of sand and gravel with geosynthetics. Therefore, RAP material can be well used as alternative backfill materials in lieu of natural aggregates such as gravel and sand in geotechnical applications. [ABSTRACT FROM AUTHOR]

Natural language understanding (NLU) is an important aspect of achieving human–machine interactions in the automotive application field, consisting of two core subtasks, multiple-intent detection, and slot filling (ID-SF). However, existing joint multiple ID-SF tasks in the Chinese automotive domain face two challenges: (1) There is a limited availability of Chinese multi-intent corpus data for research purposes in the automotive domain; (2) In the current models, the interaction between intent detection and slot filling is often unidirectional, which ultimately leads to inadequate accuracy in intent detection. A novel multi-intent parallel interactive framework based on heterogeneous graphs for the automotive applications field (Auto-HPIF) was proposed to overcome these issues. Its improvements mainly include three aspects: firstly, the incorporation of the Chinese bidirectional encoder representations from transformers (BERT) language model and Gaussian prior attention mechanism allow each word to acquire more comprehensive contextual information; secondly, the establishment of a heterogeneous graph parallel interactive network efficiently exploits intent and slot information, facilitating mutual guidance; lastly, the application of the cross-entropy loss function to the multi-intent classification task enhances the model's robustness and adaptability. Additionally, a Chinese automotive multi-intent dataset (CADS) comprising 13,100 Chinese utterances, seven types of slots, and thirty types of intents were collected and annotated. The proposed framework model demonstrates significant improvements across various datasets. On the Chinese automotive multi-intent dataset (CADS), the model achieves an overall accuracy of 87.94%, marking a notable 2.07% enhancement over the previous best baseline. Additionally, the model performs commendably on two publicly available datasets. Specifically, it showcases a 3.0% increase in overall accuracy on the MixATIS dataset and a 0.7% improvement on the MixSNIPS dataset. These findings showcase the efficacy and generalizability of the proposed model in tackling the complexity of joint multiple ID-SF tasks within the Chinese automotive domain. [ABSTRACT FROM AUTHOR]