Object recognition is among the basic survival skills of human beings and other animals. To date, artificial intelligence (AI) assisted high-performance object recognition is primarily visual-based, empowered by the rapid development of sensing and computational capabilities. Here, we report a tactile-olfactory sensing array, which was inspired by the natural sense-fusion system of star-nose mole, and can permit real-time acquisition of the local topography, stiffness, and odor of a variety of objects without visual input. The tactile-olfactory information is processed by a bioinspired olfactory-tactile associated machine-learning algorithm, essentially mimicking the biological fusion procedures in the neural system of the star-nose mole. Aiming to achieve human identification during rescue missions in challenging environments such as dark or buried scenarios, our tactile-olfactory intelligent sensing system could classify 11 typical objects with an accuracy of 96.9% in a simulated rescue scenario at a fire department test site. The tactile-olfactory bionic sensing system required no visual input and showed superior tolerance to environmental interference, highlighting its great potential for robust object recognition in difficult environments where other methods fall short.

Under high-stress conditions, rock burst disasters can significantly impact underground civil engineering construction. For underground metal mines, rock burst evaluations and prevention during mining have become major research topics, and the prediction and prevention of rock burst must be based on the study of rocks and rock burst tendencies. To further prevent the risk of geological disasters and provide timely warnings, a finite-interval cloud model based on the CRITIC algorithm is proposed in this paper to address the uncertainty of rock burst evaluation, the complexity under multi-factor interactions, and the correlations between factors, and it then realizes a preliminary qualitative judgment of rock burst disasters. This paper selects the uniaxial compressive strength σc (I1), ratio of the uniaxial compressive strength to the tensile strength σc/σt (brittleness coefficient, I2), elastic deformation energy index Wet (I3), ratio of the maximum tangential stress to the uniaxial compressive strength σθ /σc (stress coefficient, I4) of the rock, depth of the roadway H (I5), and integrity coefficient of the rock mass Kv (I6) as indicators for rock burst propensity predictions. The CRITIC algorithm is used to consider the relationships between the evaluation indicators, and it is combined with an improved cloud model to verify 20 groups of learning samples. The calculation results obtained by the prediction method are basically consistent with the actual situation. The validity of the model is tested, and then the model is applied to the Dongguashan Copper Mine in Tongling, Anhui Province, China, for rock burst evaluation.

Rock mass grading is a basic problem in the construction industry and underground engineering research. Because the index parameters that affect the rock mass quality are ambiguous and random, rock mass quality classification is often uncertain. Based on this issue, this paper selects the rock quality index RQD, rock uniaxial saturated compressive strength Rw, rock mass integrity coefficient Kv, structural surface strength coefficient K f and groundwater seepage quantity ω as quantitative evaluation indicators to construct an evaluation system. Thirty sets of data collected in China are selected as learning samples. Through the related concepts and finite interval cloud model, the characteristic parameters of the measured data are obtained, and a cloud model is generated with a forward cloud generator to achieve the transformation between qualitative and quantitative concepts. Combined with the basic knowledge of rough set theory, the weight determination problem is transformed into an attribute importance problem. To avoid zero weights in the traditional rough set approach, this paper introduces a calculation method based on the conditional information entropy, and the weight calculation method is modified to obtain the comprehensive weights. According to the principle of the maximum membership degree, the classification of rock mass quality is performed, and the rock mass quality data are determined to have different levels of comprehensive membership. A rock mass quality evaluation method based on the coupled improved rough set-cloud model is established and successfully applied for a rock mass quality evaluation of the 0+000∼0+560 test section of the second stage of underground engineering at the Guangdong Pump Storage Power Station. The results show that the model is reliable and practical and provides a new approach for uncertainty analysis and evaluation in rock engineering practice.