Construction engineering and management(CEM)has become increasingly complicated with the increasing size of engineering projects under different construction environments,motivating the digital transformation of CEM.T...Construction engineering and management(CEM)has become increasingly complicated with the increasing size of engineering projects under different construction environments,motivating the digital transformation of CEM.To contribute to a better understanding of the state of the art of smart techniques for engineering projects,this paper provides a comprehensive review of multi-criteria decision-making(MCDM)techniques,intelligent techniques,and their applications in CEM.First,a comprehensive framework detailing smart technologies for construction projects is developed.Next,the characteristics of CEM are summarized.A bibliometric review is then conducted to investigate the keywords,journals,and clusters related to the application of smart techniques in CEM during 2000-2022.Recent advancements in intelligent techniques are also discussed under the following six topics:①big data technology;②computer vision;③speech recognition;④natural language processing;⑤machine learning;and⑥knowledge representation,understanding,and reasoning.The applications of smart techniques are then illustrated via underground space exploitation.Finally,future research directions for the sustainable development of smart construction are highlighted.展开更多
Geotechnical engineering data are usually small-sample and high-dimensional,which brings a lot of challenges in predictive modeling.This paper uses a typical high-dimensional and small-sample swell pressure(P_(s))data...Geotechnical engineering data are usually small-sample and high-dimensional,which brings a lot of challenges in predictive modeling.This paper uses a typical high-dimensional and small-sample swell pressure(P_(s))dataset to explore the possibility of using multi-algorithm hybrid ensemble and dimensionality reduction methods to mitigate the uncertainty of soil parameter prediction.Based on six machine learning(ML)algorithms,the base learner pool is constructed,and four ensemble methods,Stacking(SG),Blending(BG),Voting regression(VR),and Feature weight linear stacking(FWL),are used for the multi-algorithm ensemble.Furthermore,the importance of permutation is used for feature dimensionality reduction to mitigate the impact of weakly correlated variables on predictive modeling.The results show that the proposed methods are superior to traditional prediction models and base ML models,where FWL is more suitable for modeling with small-sample datasets,and dimensionality reduction can simplify the data structure and reduce the adverse impact of the small-sample effect,which points the way to feature selection for predictive modeling.Based on the ensemble methods,the feature importance of the five primary factors affecting P_(s) is the maximum dry density(31.145%),clay fraction(15.876%),swell percent(15.289%),plasticity index(14%),and optimum moisture content(13.69%),the influence of input parameters on P_(s) is also investigated,in line with the findings of the existing literature.展开更多
Interactions between cement clinkers and clay minerals are crucial to the much lower strength of cement-based stabilized clays than concrete or mortar.In this paper,the kaolinite-based and montmorillonite-based clays ...Interactions between cement clinkers and clay minerals are crucial to the much lower strength of cement-based stabilized clays than concrete or mortar.In this paper,the kaolinite-based and montmorillonite-based clays were respectively stabilized by tricalcium silicate(C3S)and tricalcium aluminate(C3A),and measured by the unconfined compressive strength(UCS),29Si/27Al solid state nuclear magnetic resonance(SS-NMR),Fourier transform infrared spectroscopy(FTIR),and transmission electron microscope(TEM)to probe the clinker-clay mineral interaction from macro-mechanical,mineralogical,and microstructural perspectives.The results show that C3A-stabilized samples gain strength rapidly in the first 3 d but are only 20%e60%of the strength of C3S-stabilized ones after 60 d.Microstructures reveal that montmorillonite shows better pozzolanic reactivity due to its superior Sichain and lattice substitution compared to kaolinite.This interaction domains the engineering performance of stabilized clays,benefiting the design of stabilizer referring to as the industrial by-products and clay minerals.展开更多
Road transport plays a crucial role in facilitating mobility and the movement of goods,particularly in the Extended Bangkok Metropolitan Region(EBMR),Thailand.This area is undergoing rapid industrialization and urbani...Road transport plays a crucial role in facilitating mobility and the movement of goods,particularly in the Extended Bangkok Metropolitan Region(EBMR),Thailand.This area is undergoing rapid industrialization and urbanization,resulting in significant energy consumption and greenhouse gas(GHG)emissions.This study examined the relationships among individual socioeconomic factors,travel characteristics,and energy consumption characteristics and their impacts on GHG emissions from road transport.The path analysis technique was applied to identify the key driving factors and their causal relationships.The data were collected through 1600 questionnaire surveys with road drivers in representative areas of the EBMR from December 2022 to May 2023.The results revealed that individual socioeconomic factors significantly influenced GHG emissions from road transport.Among the drivers,factors such as income,age,education,and driving experience indirectly influenced travel characteristics and energy consumption characteristics,impacting GHG emissions.Similarly,individual socioeconomic factors affected the travel characteristics of tourists and personal travelers.Driving experience was a crucial factor for public road transport and freight vehicle drivers,influencing travel characteristics and contributing to GHG emissions.These findings highlight the importance of key policy recommendations,such as promoting the adoption of electric vehicles,optimizing public transport,incentivizing low-emission tourism,and modernizing freight transport with clean technologies,to enhance efficiency,reduce emissions,and support regional sustainability.This study provides policy-makers with insights into the key factors influencing GHG emissions across different driving factors,revealing how individual socioeconomic factors impact travel characteristics and energy consumption characteristics.The findings will inform the development of targeted emission reduction strategies and sustainable transport policies.展开更多
Water-related hazards, such as river floods, flash floods and droughts, are becoming more frequent in the Upper Chao Phraya River Basin, Thailand, due to climate change and urbanization, causing significant societal, ...Water-related hazards, such as river floods, flash floods and droughts, are becoming more frequent in the Upper Chao Phraya River Basin, Thailand, due to climate change and urbanization, causing significant societal, economic, and environmental damage. This study supports decision-making for nature-based solutions (NBS) to address mitigate these hazards. Using multi-criteria decision analysis, simulation modeling, and spatial analysis, the study identified precipitation and river discharges as key hazard drivers. Mapping hazard severity at various scales, the findings suggest that expanding green areas and water storage can enhance water management and reduce hazard impacts. This research offers critical insights for NBS adoption in water-related risk reduction.展开更多
An efficient determination of the geological characteristics and soil-rock type ahead of a tunnel face is critical for adjusting construction parameters during shield tunnelling.In general,operational engineers rely o...An efficient determination of the geological characteristics and soil-rock type ahead of a tunnel face is critical for adjusting construction parameters during shield tunnelling.In general,operational engineers rely on visual observations of mucky soil types from belt conveyors.This results in shield halting and involves both time and cost implications.This paper proposes a deep learning approach designed to identify mucky soil by monitoring a video installed on the strut of a belt conveyer.The proposed approach comprises four steps:(1)image acquisition,(2)enhanced you-only-look-once(YOLO)modelling,(3)model performance evaluation,and(4)soil identification based on an optimal analysis.The enhanced YOLO model is a deep image detection algorithm.It was introduced by integrating two innovative strategies:data augmentation and imbalance learning.This enhancement accelerates the speed of image identification and improves the overall classification performance.A case study of shield tunnelling in the soil-rock mixed strata of the GuangzhoueFoshan intercity railway line was conducted to validate the proposed approach.The results indicate that the enhanced YOLO model achieves a classification performance comparable to that of the highly optimised AlexNet and GoogleNet.Additionally,the proposed approach more effectively detects the muck soil content than manual observation.This demonstrates its potential for real-time applications in shield tunnelling operations.展开更多
Bangladesh aims to become a high-income country by 2041,requiring investment in critical infrastructure sectors.Disruptions in one sector can affect others,so prioritizing actions for key sectors is essential when res...Bangladesh aims to become a high-income country by 2041,requiring investment in critical infrastructure sectors.Disruptions in one sector can affect others,so prioritizing actions for key sectors is essential when resources are limited.Since no country has endless resources,the current strategy is to focus on developing infrastructure in order of importance.This means that the most critical infrastructure is given priority when allocating resources.The aim of this study was to identify the critical infrastructure sectors and their interdependencies in Bangladesh.While the science of critical infrastructure protection and resilience is well-developed in high-income and developed economies,this research sheds light on identifying critical infrastructure in developing nations like Bangladesh.To identify the critical infrastructure sectors,a comprehensive literature survey was conducted,which was verified and validated by country experts.Policymakers,practitioners,and researchers were consulted through key informant interviews(KII).Interpretive structural modeling(ISM)was applied to determine the interdependencies among identified sectors.Furthermore,cross-impact matrix multiplication applied to classification(MICMAC)analysis was applied to categorize the identified sectors based on driving power and dependence of sectors.The study found that 14 sectors-energy,information and communication technology(ICT),media and culture,law enforcement,transportation,among others-need extra protection measures.It also identified infrastructures with driving power and dependencies in the country’s context.Additionally,this article offers recommendations for improving policy and institutional actions to enhance the resilience of critical infrastructure in the country.展开更多
This paper presents an experimental study and micro-mechanism discussion on gypsum role in the mechanical improvements of cement-based stabilized clay(CBSC).A soft marine clay at two initial water contents(i.e.50%and ...This paper presents an experimental study and micro-mechanism discussion on gypsum role in the mechanical improvements of cement-based stabilized clay(CBSC).A soft marine clay at two initial water contents(i.e.50%and 70%)was treated by reconstituted cementitious binders with varying gypsum to clinker(G/C)ratios and added metakaolin to facilitate the formation of ettringite,followed by the measurements of final water contents,dry densities and strengths in accordance with ASTM standards as well as microstructure by mercury intrusion porosimetry(MIP)and scanning electron microscopy(SEM).Results reveal that the gypsum fraction has a significant influence on the index and mechanical properties of the CBSC,and there exists a threshold of the G/C ratio,which is 10%and 15%for clays with 50%and 70%initial water contents,respectively.Beyond which adding excessive gypsum cannot improve the strength further,eliminating the beneficial role.At these thresholds of the G/C ratio,the unconfined compressive strength(UCS)values for clays with 50%and 70%initial water contents are 1.74 MPa and 1.53 MPa at 60 d of curing,respectively.Microstructure characterization shows that,besides the common cementation-induced strengthening,newly formed ettringite also acts as significant pore infills,and the associated remarkable volumetric expansion is responsible,and may be the primary factor,for the beneficial strength gain due to the added gypsum.Moreover,pore-filling ettringite also leads to the conversion of relatively large inter-aggregate to smaller intra-aggregate pores,thereby causing a more homogeneous matrix or solid skeleton with higher strength.Overall,added gypsum plays a vital beneficial role in the strength development of the CBSC,especially for very soft clays.展开更多
The study proposes an improved Harris hawks optimization(IHHO) algorithm by integrating the standard Harris hawks optimization(HHO) algorithm and mutation-based search mechanism for developing a high-performance machi...The study proposes an improved Harris hawks optimization(IHHO) algorithm by integrating the standard Harris hawks optimization(HHO) algorithm and mutation-based search mechanism for developing a high-performance machine learning solution for predicting soil compression index. HHO is a newly introduced meta-heuristic optimization algorithm(MOA) used to solve continuous search problems.Compared to the original HHO, the proposed IHHO can evade trapping in local optima, which in turn raises the search capabilities and enhances the search mechanism relying on mutation. Subsequently, a novel meta-heuristic-based soft computing technique called ELM-IHHO was established by integrating IHHO and extreme learning machine(ELM) to estimate soil compression index. A sum of 688 consolidation test data was collected for this purpose from an ongoing dedicated freight corridor railway project. To evaluate the generalization capability of the proposed ELM-IHHO model, a detailed comparison between ELM-IHHO and other well-established MOAs, such as particle swarm optimization,genetic algorithm, and biogeography-based optimization integrated with ELM, was performed. Based on the outcomes, the ELM-IHHO model exhibits superior performance over the other MOAs in predicting soil compression index.展开更多
This study focuses on the consolidation behavior and mathematical interpretation of partially-saturated ground improved by impervious column inclusion.The constitutive relations for soil skeleton,pore air and pore wat...This study focuses on the consolidation behavior and mathematical interpretation of partially-saturated ground improved by impervious column inclusion.The constitutive relations for soil skeleton,pore air and pore water for partially saturated soils are proposed in the context of partially-saturated ground improved by impervious column inclusion.Settlement equation and dissipation equations of excess pore air/water pressures for a partially saturated improved ground are then derived.The semi-analytical solutions for ground settlement and pore pressure dissipation are then obtained through the Laplace transform and validated by the existing solutions for two special cases in the literature and the numerical results obtained from the finite difference method.A series of parametric studies is finally conducted to investigate the influence of some key factors on consolidation of partially saturated ground improved by impervious column inclusion.Based on the parametric study,it can be found that a higher value of the area replacement ratio or modulus of the pile results in a longer dissipation time of excess pore air pressure(PAP),a shorter dissipation time of excess pore water pressure(PWP),and a lower normalized settlement.展开更多
As massive underground projects have become popular in dense urban cities,a problem has arisen:which model predicts the best for Tunnel Boring Machine(TBM)performance in these tunneling projects?However,performance le...As massive underground projects have become popular in dense urban cities,a problem has arisen:which model predicts the best for Tunnel Boring Machine(TBM)performance in these tunneling projects?However,performance level of TBMs in complex geological conditions is still a great challenge for practitioners and researchers.On the other hand,a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction schedule.The performance of TBM is very difficult to estimate due to various geotechnical and geological factors and machine specifications.The previously-proposed intelligent techniques in this field are mostly based on a single or base model with a low level of accuracy.Hence,this study aims to introduce a hybrid randomforest(RF)technique optimized by global harmony search with generalized oppositionbased learning(GOGHS)for forecasting TBM advance rate(AR).Optimizing the RF hyper-parameters in terms of,e.g.,tree number and maximum tree depth is the main objective of using the GOGHS-RF model.In the modelling of this study,a comprehensive databasewith themost influential parameters onTBMtogetherwithTBM AR were used as input and output variables,respectively.To examine the capability and power of the GOGHSRF model,three more hybrid models of particle swarm optimization-RF,genetic algorithm-RF and artificial bee colony-RF were also constructed to forecast TBM AR.Evaluation of the developed models was performed by calculating several performance indices,including determination coefficient(R2),root-mean-square-error(RMSE),and mean-absolute-percentage-error(MAPE).The results showed that theGOGHS-RF is a more accurate technique for estimatingTBMAR compared to the other applied models.The newly-developedGOGHS-RFmodel enjoyed R2=0.9937 and 0.9844,respectively,for train and test stages,which are higher than a pre-developed RF.Also,the importance of the input parameters was interpreted through the SHapley Additive exPlanations(SHAP)method,and it was found that thrust force per cutter is the most important variable on TBMAR.The GOGHS-RF model can be used in mechanized tunnel projects for predicting and checking performance.展开更多
This study implements a hybrid ensemble machine learning method for forecasting the rate of penetration(ROP) of tunnel boring machine(TBM),which is becoming a prerequisite for reliable cost assessment and project sche...This study implements a hybrid ensemble machine learning method for forecasting the rate of penetration(ROP) of tunnel boring machine(TBM),which is becoming a prerequisite for reliable cost assessment and project scheduling in tunnelling and underground projects in a rock environment.For this purpose,a sum of 185 datasets was collected from the literature and used to predict the ROP of TBM.Initially,the main dataset was utilised to construct and validate four conventional soft computing(CSC)models,i.e.minimax probability machine regression,relevance vector machine,extreme learning machine,and functional network.Consequently,the estimated outputs of CSC models were united and trained using an artificial neural network(ANN) to construct a hybrid ensemble model(HENSM).The outcomes of the proposed HENSM are superior to other CSC models employed in this study.Based on the experimental results(training RMSE=0.0283 and testing RMSE=0.0418),the newly proposed HENSM is potential to assist engineers in predicting ROP of TBM in the design phase of tunnelling and underground projects.展开更多
This study presents a numerical investigation of dewatering-induced settlement and wall deflection during pumping tests in Tianjin,China.Based on the measured groundwater head and building settlement during the pumpin...This study presents a numerical investigation of dewatering-induced settlement and wall deflection during pumping tests in Tianjin,China.Based on the measured groundwater head and building settlement during the pumping test,a three-dimensional liquid-solid coupling model is established by using the finite element method(FEM).The void ratio,hydraulic conductivity,and elastic modulus of each layer are back-calculated through the numerical model.The groundwater drawdown,seepage field,ground settlement,horizontal ground displacement,and diaphragm wall lateral deflection are analyzed using the FEM model.The simulated results demonstrate that(i)the maximum ground settlement outside of the excavation reaches to 82 mm due to the leakage effect of aquitards;(ii)large horizontal displacement occurs in the soil during the pumping test with a maximum value of 28.3 mm,and the installation of the diaphragm wall in the aquifer can reduce the horizontal displacement of the ground;(iii)long-term pumping causes a large lateral deflection of the diaphragm wall,and a maximum value of 23.2 mm occurs at the layer where the screens of the wells are located;and(iv)long-term large-scale pumping should be avoided before excavation.展开更多
Road pavement surfaces need routine and regular monitoring and inspection to keep the surface layers in high-quality condition.However,the population growth and the increases in the number of vehicles and the length o...Road pavement surfaces need routine and regular monitoring and inspection to keep the surface layers in high-quality condition.However,the population growth and the increases in the number of vehicles and the length of road networks worldwide have required researchers to identify appropriate and accurate road pavement monitoring techniques.The vibration-based technique is one of the effective techniques used to measure the condition of pavement degradation and the level of pavement roughness.The consistency of pavement vibration data is directly proportional to the intensity of surface roughness.Intense fluctuations in vibration signals indicate possible defects at certain points of road pavement.However,vibration signals typically need a series of pre-processing techniques such as filtering,smoothing,segmentation,and labelling before being used in advanced processing and analyses.This research reports the use of noise-cancelling and datasmoothing techniques,including high pass filter,moving average method,median,Savitzky-Golay filter,and extracting peak envelope method,to enhance raw vibration signals for further processing and classification.The results show significant variations in the impact of noise-cancelling and data-smoothing techniques on raw pavement vibration signals.According to the results,the high pass filter is a more accurate noise-cancelling and data smoothing technique on road pavement vibration data compared to other data filtering and data smoothing methods.展开更多
Road safety is the most important feature of a modern city,and it affects almost everyone in the community,especially vulnerable road users(VRUs).This paper comprehensively examines existing scientific literature rega...Road safety is the most important feature of a modern city,and it affects almost everyone in the community,especially vulnerable road users(VRUs).This paper comprehensively examines existing scientific literature regarding contemporary methodologies for collect-ing data in safety studies involving VRUs.The objective is to compile a comprehensive list of data collection methods,recognize potential applications of emerging technologies,and categorize them based on a novel taxonomy.A preferred reporting items for systematic reviews and meta analyses(PRISMA)flowchart is used to conduct the systematic literature search by setting some inclusion and exclusion criteria.Different keyword searches are used in Scopus and Web of Science databases,followed by relevant references and citation analysis to find eligible papers subject to a full-text peer review.Finally,the identified papers are categorized and analyzed based on the technology type they used.8374 and 109 papers have been identified from the initial search and the forward and backward snowballing,respectively.167 documents have been selected to carry out full-text reviews,with 135 finally included in the study.The technology employed in safety research for VRUs,including cameras,sensors,trackers,mobile phones,social media,drones,and eye-tracking devices has also been included in the classification of identified documents.Commonly employed methods for collecting data on VRUs include camera-based,sensor-based,and tracker-based approaches.The mobile phone-based approach has been least common for collecting data on pedestrians’safety because of distractions.In recent years,social media-based,drone,and eye-tracking approaches have become widely uti-lized for collecting and analyzing data.Recently,multiple approaches have been employed for data collection.The documents predominantly have addressed the movements,behav-iors,emotions,and route choices of pedestrians.Similarly,documents related to cyclists have been mainly concerned with obstacle detection,analysis of cyclists’behavior,and guiding cyclists.展开更多
This paper proposed a framework for muck types identification based on data augmentation-assisted image recognition during shield tunnelling.The muck pictures were collected from the shield monitoring system above the...This paper proposed a framework for muck types identification based on data augmentation-assisted image recognition during shield tunnelling.The muck pictures were collected from the shield monitoring system above the conveyor belt.The data augmentation operations were then used to increase the quality of the original images.Furthermore,the Bayesian optimisation algorithm was employed to adjust the parameters of augmenters and highlight the features of the photos.The deep image recognition algorithms(AlexNet and GoogLeNet)were trained and enhanced by the augmentation images,which were used to establish the muck types identification models and assessed by the evaluation indices.Model efficiency was analysed through the performance and time cost of training and validation processes to select the optimal model for muck types identification.Results showed that the performance of identification models could be highly increased by data augmentation with Bayesian optimisation,and the enhanced GoogLeNet performed the highest efficiency for muck types identification.展开更多
The efficiency of gas hydrate production depends on the success of gas exploration and occurrence evaluation.The existing evaluation models are generally univariate and only applicable to certain geological settings.T...The efficiency of gas hydrate production depends on the success of gas exploration and occurrence evaluation.The existing evaluation models are generally univariate and only applicable to certain geological settings.This study presents a holistic approach to evaluate the likelihood of gas hydrate occurrence by supplying an index for mapping gas hydrate levels with depth.The approach integrates a generalised TOPSIS method with the fuzzy set theory.An expedition of gas hydrate conducted in the Shenhu area of the South China Sea was adopted as a case study to assess the reliability of the proposed index.As a multivariate model,the proposed approach enables the capture of non-linearity associated with gas hydrates in its entirety.The magnitude of the strength of the influential factor varies substantially from one site to another across the Shenhu area.The results also show that no site achieves the highest likelihood‘Level V’.These results are consistent with the gas saturation values obtained using Archie’s relationship.For example,at SH4 and SH7,the values of the likelihood index are the highest between 170–185 m and 150–165 m,respectively,and the observed saturation at these locations varies from 20%(SH4)to 43%(SH7).The proposed likelihood index yields a prominent ability to quantify the level of occurrence of gas hydrates with depth at different sites.It appears to be an efficient multicriteria system bound to improve the management of the gas production trial stage.展开更多
Properties of aggregates are majorly influenced by parameters of source rocks viz.,formation process,chemical composition,impurities,volume of pores,and grain size.The study presents a review of aggregate treatment me...Properties of aggregates are majorly influenced by parameters of source rocks viz.,formation process,chemical composition,impurities,volume of pores,and grain size.The study presents a review of aggregate treatment methods and its efficacy to enhance the quality of aggregate.Various aspects of aggregate treatment methods like processing temperature,the dosage of additives,adaptability in the field is studied for three treatment methods viz.,polymer coating,cementitious coating,and chemical treatments.The paper also presents an insight to understand the effect of different treatment methods on mix properties and performance parameters of asphalt mixes.The review revealed that the shape properties of aggregates can be enhanced by the incorporating suitable crushing process(two-stage or three-stage).Whereas,physical and durability properties of aggregates can be improved by various treatment methods like polymer coating,Zycosoil treatment.It was further inferred from the review that treatment methods can have moderate effects on the mechanical properties of aggregates,since,it is mostly dependent on properties of source rocks.展开更多
This paper presents an analysis of a tunnel failure accident during shield tunnel construction on Foshan Metro Line 2 in China.The failure is caused by the leakage of the multilayer seal system,which consists of sever...This paper presents an analysis of a tunnel failure accident during shield tunnel construction on Foshan Metro Line 2 in China.The failure is caused by the leakage of the multilayer seal system,which consists of several brush seals at the tail of the shield.Four different failure modes for the multilayer seal system are discussed.A simple structural analysis of the brush seals is then conducted,and failure mode 4(failure due to brush seal deformation)is identified as a major reason for the Foshan tunnel accident.A finite element method(FEM)analysis is employed to validate the conclusions drawn from the simple structural analysis of the brush seals.展开更多
Ceiling gas temperature rise is an important evaluation indicator determining the level of risk in a subway tunnel fire.However,very little literature has been found that has addressed the emergency when a fired subwa...Ceiling gas temperature rise is an important evaluation indicator determining the level of risk in a subway tunnel fire.However,very little literature has been found that has addressed the emergency when a fired subway train with lateral multiple openings stops in the interval tunnel.Hence,a battery of full-scale numerical simulations were employed to address the impact of train fire location on the gas temperature beneath the train ceiling.Numerical results showed that the ceiling gas temperature rise is affected by the pressure difference on both sides of fire source and the backflow from the end wall,which depends on the heat release rate and the fire location.The ceiling gas temperature rise decays exponentially in the process of longitudinal spread,and it can be predicted by a dimensionless model with a sum of two exponential equations.Finally,based on a critical fire location(L'cr=0.667),two exponential equations were developed to quantitatively express the influences of the fire size and the fire location on the maximum ceiling gas temperature.The research results can be utilized for providing an initial understanding of the smoke propagation in a subway train fire.展开更多
基金funded by the project of Guangdong Provincial Basic and Applied Basic Research Fund Committee(2022A1515240073)the Pearl River Talent Recruitment Program(2019CX01G338),Guangdong Province.
文摘Construction engineering and management(CEM)has become increasingly complicated with the increasing size of engineering projects under different construction environments,motivating the digital transformation of CEM.To contribute to a better understanding of the state of the art of smart techniques for engineering projects,this paper provides a comprehensive review of multi-criteria decision-making(MCDM)techniques,intelligent techniques,and their applications in CEM.First,a comprehensive framework detailing smart technologies for construction projects is developed.Next,the characteristics of CEM are summarized.A bibliometric review is then conducted to investigate the keywords,journals,and clusters related to the application of smart techniques in CEM during 2000-2022.Recent advancements in intelligent techniques are also discussed under the following six topics:①big data technology;②computer vision;③speech recognition;④natural language processing;⑤machine learning;and⑥knowledge representation,understanding,and reasoning.The applications of smart techniques are then illustrated via underground space exploitation.Finally,future research directions for the sustainable development of smart construction are highlighted.
基金great gratitude to National Key Research and Development Project(Grant No.2019YFC1509800)for their financial supportNational Nature Science Foundation of China(Grant No.12172211)for their financial support.
文摘Geotechnical engineering data are usually small-sample and high-dimensional,which brings a lot of challenges in predictive modeling.This paper uses a typical high-dimensional and small-sample swell pressure(P_(s))dataset to explore the possibility of using multi-algorithm hybrid ensemble and dimensionality reduction methods to mitigate the uncertainty of soil parameter prediction.Based on six machine learning(ML)algorithms,the base learner pool is constructed,and four ensemble methods,Stacking(SG),Blending(BG),Voting regression(VR),and Feature weight linear stacking(FWL),are used for the multi-algorithm ensemble.Furthermore,the importance of permutation is used for feature dimensionality reduction to mitigate the impact of weakly correlated variables on predictive modeling.The results show that the proposed methods are superior to traditional prediction models and base ML models,where FWL is more suitable for modeling with small-sample datasets,and dimensionality reduction can simplify the data structure and reduce the adverse impact of the small-sample effect,which points the way to feature selection for predictive modeling.Based on the ensemble methods,the feature importance of the five primary factors affecting P_(s) is the maximum dry density(31.145%),clay fraction(15.876%),swell percent(15.289%),plasticity index(14%),and optimum moisture content(13.69%),the influence of input parameters on P_(s) is also investigated,in line with the findings of the existing literature.
基金supported by the National Natural Science Foundation of China(Grant Nos.52278334,42272322,and 52209136).
文摘Interactions between cement clinkers and clay minerals are crucial to the much lower strength of cement-based stabilized clays than concrete or mortar.In this paper,the kaolinite-based and montmorillonite-based clays were respectively stabilized by tricalcium silicate(C3S)and tricalcium aluminate(C3A),and measured by the unconfined compressive strength(UCS),29Si/27Al solid state nuclear magnetic resonance(SS-NMR),Fourier transform infrared spectroscopy(FTIR),and transmission electron microscope(TEM)to probe the clinker-clay mineral interaction from macro-mechanical,mineralogical,and microstructural perspectives.The results show that C3A-stabilized samples gain strength rapidly in the first 3 d but are only 20%e60%of the strength of C3S-stabilized ones after 60 d.Microstructures reveal that montmorillonite shows better pozzolanic reactivity due to its superior Sichain and lattice substitution compared to kaolinite.This interaction domains the engineering performance of stabilized clays,benefiting the design of stabilizer referring to as the industrial by-products and clay minerals.
基金the Royal Thai Government(RTG)provided financing for this study,as well as a scholarship to assist PhD studies at the Asian Institute of Technology(AIT)The National Science and Technology Development Agency(NSTDA)of Thailand via the Development of High-Quality Research Graduates in Science and Technology Project,a collaboration between NSTDA and AIT, also offers a top-up scholarship for this study
文摘Road transport plays a crucial role in facilitating mobility and the movement of goods,particularly in the Extended Bangkok Metropolitan Region(EBMR),Thailand.This area is undergoing rapid industrialization and urbanization,resulting in significant energy consumption and greenhouse gas(GHG)emissions.This study examined the relationships among individual socioeconomic factors,travel characteristics,and energy consumption characteristics and their impacts on GHG emissions from road transport.The path analysis technique was applied to identify the key driving factors and their causal relationships.The data were collected through 1600 questionnaire surveys with road drivers in representative areas of the EBMR from December 2022 to May 2023.The results revealed that individual socioeconomic factors significantly influenced GHG emissions from road transport.Among the drivers,factors such as income,age,education,and driving experience indirectly influenced travel characteristics and energy consumption characteristics,impacting GHG emissions.Similarly,individual socioeconomic factors affected the travel characteristics of tourists and personal travelers.Driving experience was a crucial factor for public road transport and freight vehicle drivers,influencing travel characteristics and contributing to GHG emissions.These findings highlight the importance of key policy recommendations,such as promoting the adoption of electric vehicles,optimizing public transport,incentivizing low-emission tourism,and modernizing freight transport with clean technologies,to enhance efficiency,reduce emissions,and support regional sustainability.This study provides policy-makers with insights into the key factors influencing GHG emissions across different driving factors,revealing how individual socioeconomic factors impact travel characteristics and energy consumption characteristics.The findings will inform the development of targeted emission reduction strategies and sustainable transport policies.
文摘Water-related hazards, such as river floods, flash floods and droughts, are becoming more frequent in the Upper Chao Phraya River Basin, Thailand, due to climate change and urbanization, causing significant societal, economic, and environmental damage. This study supports decision-making for nature-based solutions (NBS) to address mitigate these hazards. Using multi-criteria decision analysis, simulation modeling, and spatial analysis, the study identified precipitation and river discharges as key hazard drivers. Mapping hazard severity at various scales, the findings suggest that expanding green areas and water storage can enhance water management and reduce hazard impacts. This research offers critical insights for NBS adoption in water-related risk reduction.
基金funded by Guangdong Province Scientific Research Project for Young Innovation Talent(Grant No.2022KQNCX239)The Pearl River Talent Recruitment Program(Grant No.2019CX01G338),Guangdong Province.
文摘An efficient determination of the geological characteristics and soil-rock type ahead of a tunnel face is critical for adjusting construction parameters during shield tunnelling.In general,operational engineers rely on visual observations of mucky soil types from belt conveyors.This results in shield halting and involves both time and cost implications.This paper proposes a deep learning approach designed to identify mucky soil by monitoring a video installed on the strut of a belt conveyer.The proposed approach comprises four steps:(1)image acquisition,(2)enhanced you-only-look-once(YOLO)modelling,(3)model performance evaluation,and(4)soil identification based on an optimal analysis.The enhanced YOLO model is a deep image detection algorithm.It was introduced by integrating two innovative strategies:data augmentation and imbalance learning.This enhancement accelerates the speed of image identification and improves the overall classification performance.A case study of shield tunnelling in the soil-rock mixed strata of the GuangzhoueFoshan intercity railway line was conducted to validate the proposed approach.The results indicate that the enhanced YOLO model achieves a classification performance comparable to that of the highly optimised AlexNet and GoogleNet.Additionally,the proposed approach more effectively detects the muck soil content than manual observation.This demonstrates its potential for real-time applications in shield tunnelling operations.
基金partial scholarship support under the EDITS-AIT projectThe EDITS-AIT project at the Asian Institute of Technology, Thailand, received funding from the Energy Demand changes Induced by Technological and Social innovations (EDITS) project, which is part of the initiative coordinated by the Research Institute of Innovative Technology for the Earth (RITE) and the International Institute for Applied Systems Analysis (IIASA) (and funded by the Ministry of Economy, Trade, and Industry (METI), Japan)
文摘Bangladesh aims to become a high-income country by 2041,requiring investment in critical infrastructure sectors.Disruptions in one sector can affect others,so prioritizing actions for key sectors is essential when resources are limited.Since no country has endless resources,the current strategy is to focus on developing infrastructure in order of importance.This means that the most critical infrastructure is given priority when allocating resources.The aim of this study was to identify the critical infrastructure sectors and their interdependencies in Bangladesh.While the science of critical infrastructure protection and resilience is well-developed in high-income and developed economies,this research sheds light on identifying critical infrastructure in developing nations like Bangladesh.To identify the critical infrastructure sectors,a comprehensive literature survey was conducted,which was verified and validated by country experts.Policymakers,practitioners,and researchers were consulted through key informant interviews(KII).Interpretive structural modeling(ISM)was applied to determine the interdependencies among identified sectors.Furthermore,cross-impact matrix multiplication applied to classification(MICMAC)analysis was applied to categorize the identified sectors based on driving power and dependence of sectors.The study found that 14 sectors-energy,information and communication technology(ICT),media and culture,law enforcement,transportation,among others-need extra protection measures.It also identified infrastructures with driving power and dependencies in the country’s context.Additionally,this article offers recommendations for improving policy and institutional actions to enhance the resilience of critical infrastructure in the country.
基金supported by the National Key R&D Program of China (Grant No. 2019YFC1806004)National Natural Science Foundation of China (Grant Nos. 51878159 and 41572280)
文摘This paper presents an experimental study and micro-mechanism discussion on gypsum role in the mechanical improvements of cement-based stabilized clay(CBSC).A soft marine clay at two initial water contents(i.e.50%and 70%)was treated by reconstituted cementitious binders with varying gypsum to clinker(G/C)ratios and added metakaolin to facilitate the formation of ettringite,followed by the measurements of final water contents,dry densities and strengths in accordance with ASTM standards as well as microstructure by mercury intrusion porosimetry(MIP)and scanning electron microscopy(SEM).Results reveal that the gypsum fraction has a significant influence on the index and mechanical properties of the CBSC,and there exists a threshold of the G/C ratio,which is 10%and 15%for clays with 50%and 70%initial water contents,respectively.Beyond which adding excessive gypsum cannot improve the strength further,eliminating the beneficial role.At these thresholds of the G/C ratio,the unconfined compressive strength(UCS)values for clays with 50%and 70%initial water contents are 1.74 MPa and 1.53 MPa at 60 d of curing,respectively.Microstructure characterization shows that,besides the common cementation-induced strengthening,newly formed ettringite also acts as significant pore infills,and the associated remarkable volumetric expansion is responsible,and may be the primary factor,for the beneficial strength gain due to the added gypsum.Moreover,pore-filling ettringite also leads to the conversion of relatively large inter-aggregate to smaller intra-aggregate pores,thereby causing a more homogeneous matrix or solid skeleton with higher strength.Overall,added gypsum plays a vital beneficial role in the strength development of the CBSC,especially for very soft clays.
文摘The study proposes an improved Harris hawks optimization(IHHO) algorithm by integrating the standard Harris hawks optimization(HHO) algorithm and mutation-based search mechanism for developing a high-performance machine learning solution for predicting soil compression index. HHO is a newly introduced meta-heuristic optimization algorithm(MOA) used to solve continuous search problems.Compared to the original HHO, the proposed IHHO can evade trapping in local optima, which in turn raises the search capabilities and enhances the search mechanism relying on mutation. Subsequently, a novel meta-heuristic-based soft computing technique called ELM-IHHO was established by integrating IHHO and extreme learning machine(ELM) to estimate soil compression index. A sum of 688 consolidation test data was collected for this purpose from an ongoing dedicated freight corridor railway project. To evaluate the generalization capability of the proposed ELM-IHHO model, a detailed comparison between ELM-IHHO and other well-established MOAs, such as particle swarm optimization,genetic algorithm, and biogeography-based optimization integrated with ELM, was performed. Based on the outcomes, the ELM-IHHO model exhibits superior performance over the other MOAs in predicting soil compression index.
基金The financial support from National Natural Science Foundation of China (Grant Nos. 12172211 and 52078021)Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, China (Grant No. R201904)
文摘This study focuses on the consolidation behavior and mathematical interpretation of partially-saturated ground improved by impervious column inclusion.The constitutive relations for soil skeleton,pore air and pore water for partially saturated soils are proposed in the context of partially-saturated ground improved by impervious column inclusion.Settlement equation and dissipation equations of excess pore air/water pressures for a partially saturated improved ground are then derived.The semi-analytical solutions for ground settlement and pore pressure dissipation are then obtained through the Laplace transform and validated by the existing solutions for two special cases in the literature and the numerical results obtained from the finite difference method.A series of parametric studies is finally conducted to investigate the influence of some key factors on consolidation of partially saturated ground improved by impervious column inclusion.Based on the parametric study,it can be found that a higher value of the area replacement ratio or modulus of the pile results in a longer dissipation time of excess pore air pressure(PAP),a shorter dissipation time of excess pore water pressure(PWP),and a lower normalized settlement.
基金the National Natural Science Foundation of China(Grant 42177164)the Distinguished Youth Science Foundation of Hunan Province of China(2022JJ10073).
文摘As massive underground projects have become popular in dense urban cities,a problem has arisen:which model predicts the best for Tunnel Boring Machine(TBM)performance in these tunneling projects?However,performance level of TBMs in complex geological conditions is still a great challenge for practitioners and researchers.On the other hand,a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction schedule.The performance of TBM is very difficult to estimate due to various geotechnical and geological factors and machine specifications.The previously-proposed intelligent techniques in this field are mostly based on a single or base model with a low level of accuracy.Hence,this study aims to introduce a hybrid randomforest(RF)technique optimized by global harmony search with generalized oppositionbased learning(GOGHS)for forecasting TBM advance rate(AR).Optimizing the RF hyper-parameters in terms of,e.g.,tree number and maximum tree depth is the main objective of using the GOGHS-RF model.In the modelling of this study,a comprehensive databasewith themost influential parameters onTBMtogetherwithTBM AR were used as input and output variables,respectively.To examine the capability and power of the GOGHSRF model,three more hybrid models of particle swarm optimization-RF,genetic algorithm-RF and artificial bee colony-RF were also constructed to forecast TBM AR.Evaluation of the developed models was performed by calculating several performance indices,including determination coefficient(R2),root-mean-square-error(RMSE),and mean-absolute-percentage-error(MAPE).The results showed that theGOGHS-RF is a more accurate technique for estimatingTBMAR compared to the other applied models.The newly-developedGOGHS-RFmodel enjoyed R2=0.9937 and 0.9844,respectively,for train and test stages,which are higher than a pre-developed RF.Also,the importance of the input parameters was interpreted through the SHapley Additive exPlanations(SHAP)method,and it was found that thrust force per cutter is the most important variable on TBMAR.The GOGHS-RF model can be used in mechanized tunnel projects for predicting and checking performance.
文摘This study implements a hybrid ensemble machine learning method for forecasting the rate of penetration(ROP) of tunnel boring machine(TBM),which is becoming a prerequisite for reliable cost assessment and project scheduling in tunnelling and underground projects in a rock environment.For this purpose,a sum of 185 datasets was collected from the literature and used to predict the ROP of TBM.Initially,the main dataset was utilised to construct and validate four conventional soft computing(CSC)models,i.e.minimax probability machine regression,relevance vector machine,extreme learning machine,and functional network.Consequently,the estimated outputs of CSC models were united and trained using an artificial neural network(ANN) to construct a hybrid ensemble model(HENSM).The outcomes of the proposed HENSM are superior to other CSC models employed in this study.Based on the experimental results(training RMSE=0.0283 and testing RMSE=0.0418),the newly proposed HENSM is potential to assist engineers in predicting ROP of TBM in the design phase of tunnelling and underground projects.
基金funded by the National Nature Science Foundation of China(NSFC)(Grant No.41807235)funded by“The Pearl River Talent Recruitment Program”in 2019(Grant No.2019CX01G338)Guangdong Province and the Research Funding of Shantou University for New Faculty Member(NTF19024-2019).
文摘This study presents a numerical investigation of dewatering-induced settlement and wall deflection during pumping tests in Tianjin,China.Based on the measured groundwater head and building settlement during the pumping test,a three-dimensional liquid-solid coupling model is established by using the finite element method(FEM).The void ratio,hydraulic conductivity,and elastic modulus of each layer are back-calculated through the numerical model.The groundwater drawdown,seepage field,ground settlement,horizontal ground displacement,and diaphragm wall lateral deflection are analyzed using the FEM model.The simulated results demonstrate that(i)the maximum ground settlement outside of the excavation reaches to 82 mm due to the leakage effect of aquitards;(ii)large horizontal displacement occurs in the soil during the pumping test with a maximum value of 28.3 mm,and the installation of the diaphragm wall in the aquifer can reduce the horizontal displacement of the ground;(iii)long-term pumping causes a large lateral deflection of the diaphragm wall,and a maximum value of 23.2 mm occurs at the layer where the screens of the wells are located;and(iv)long-term large-scale pumping should be avoided before excavation.
文摘Road pavement surfaces need routine and regular monitoring and inspection to keep the surface layers in high-quality condition.However,the population growth and the increases in the number of vehicles and the length of road networks worldwide have required researchers to identify appropriate and accurate road pavement monitoring techniques.The vibration-based technique is one of the effective techniques used to measure the condition of pavement degradation and the level of pavement roughness.The consistency of pavement vibration data is directly proportional to the intensity of surface roughness.Intense fluctuations in vibration signals indicate possible defects at certain points of road pavement.However,vibration signals typically need a series of pre-processing techniques such as filtering,smoothing,segmentation,and labelling before being used in advanced processing and analyses.This research reports the use of noise-cancelling and datasmoothing techniques,including high pass filter,moving average method,median,Savitzky-Golay filter,and extracting peak envelope method,to enhance raw vibration signals for further processing and classification.The results show significant variations in the impact of noise-cancelling and data-smoothing techniques on raw pavement vibration signals.According to the results,the high pass filter is a more accurate noise-cancelling and data smoothing technique on road pavement vibration data compared to other data filtering and data smoothing methods.
文摘Road safety is the most important feature of a modern city,and it affects almost everyone in the community,especially vulnerable road users(VRUs).This paper comprehensively examines existing scientific literature regarding contemporary methodologies for collect-ing data in safety studies involving VRUs.The objective is to compile a comprehensive list of data collection methods,recognize potential applications of emerging technologies,and categorize them based on a novel taxonomy.A preferred reporting items for systematic reviews and meta analyses(PRISMA)flowchart is used to conduct the systematic literature search by setting some inclusion and exclusion criteria.Different keyword searches are used in Scopus and Web of Science databases,followed by relevant references and citation analysis to find eligible papers subject to a full-text peer review.Finally,the identified papers are categorized and analyzed based on the technology type they used.8374 and 109 papers have been identified from the initial search and the forward and backward snowballing,respectively.167 documents have been selected to carry out full-text reviews,with 135 finally included in the study.The technology employed in safety research for VRUs,including cameras,sensors,trackers,mobile phones,social media,drones,and eye-tracking devices has also been included in the classification of identified documents.Commonly employed methods for collecting data on VRUs include camera-based,sensor-based,and tracker-based approaches.The mobile phone-based approach has been least common for collecting data on pedestrians’safety because of distractions.In recent years,social media-based,drone,and eye-tracking approaches have become widely uti-lized for collecting and analyzing data.Recently,multiple approaches have been employed for data collection.The documents predominantly have addressed the movements,behav-iors,emotions,and route choices of pedestrians.Similarly,documents related to cyclists have been mainly concerned with obstacle detection,analysis of cyclists’behavior,and guiding cyclists.
基金funded by the Guangdong Provincial Basic and Applied Basic Research Fund Committee(2022A1515240073)“The Pearl River Talent Recruitment Program”in 2019(Grant No.2019CX01G338),Guangdong Province,China.
文摘This paper proposed a framework for muck types identification based on data augmentation-assisted image recognition during shield tunnelling.The muck pictures were collected from the shield monitoring system above the conveyor belt.The data augmentation operations were then used to increase the quality of the original images.Furthermore,the Bayesian optimisation algorithm was employed to adjust the parameters of augmenters and highlight the features of the photos.The deep image recognition algorithms(AlexNet and GoogLeNet)were trained and enhanced by the augmentation images,which were used to establish the muck types identification models and assessed by the evaluation indices.Model efficiency was analysed through the performance and time cost of training and validation processes to select the optimal model for muck types identification.Results showed that the performance of identification models could be highly increased by data augmentation with Bayesian optimisation,and the enhanced GoogLeNet performed the highest efficiency for muck types identification.
基金funded by“The Pearl River Talent Recruitment Program”in 2019(Grant No.2019CX01G338)Guangdong Province and the Scientific Research Initiation Grant of Shantou University for New Faculty Member(Grant No.NTF19024-2019).
文摘The efficiency of gas hydrate production depends on the success of gas exploration and occurrence evaluation.The existing evaluation models are generally univariate and only applicable to certain geological settings.This study presents a holistic approach to evaluate the likelihood of gas hydrate occurrence by supplying an index for mapping gas hydrate levels with depth.The approach integrates a generalised TOPSIS method with the fuzzy set theory.An expedition of gas hydrate conducted in the Shenhu area of the South China Sea was adopted as a case study to assess the reliability of the proposed index.As a multivariate model,the proposed approach enables the capture of non-linearity associated with gas hydrates in its entirety.The magnitude of the strength of the influential factor varies substantially from one site to another across the Shenhu area.The results also show that no site achieves the highest likelihood‘Level V’.These results are consistent with the gas saturation values obtained using Archie’s relationship.For example,at SH4 and SH7,the values of the likelihood index are the highest between 170–185 m and 150–165 m,respectively,and the observed saturation at these locations varies from 20%(SH4)to 43%(SH7).The proposed likelihood index yields a prominent ability to quantify the level of occurrence of gas hydrates with depth at different sites.It appears to be an efficient multicriteria system bound to improve the management of the gas production trial stage.
基金National Highways Authority of India(NHAI)for providing financial support to carry out this research。
文摘Properties of aggregates are majorly influenced by parameters of source rocks viz.,formation process,chemical composition,impurities,volume of pores,and grain size.The study presents a review of aggregate treatment methods and its efficacy to enhance the quality of aggregate.Various aspects of aggregate treatment methods like processing temperature,the dosage of additives,adaptability in the field is studied for three treatment methods viz.,polymer coating,cementitious coating,and chemical treatments.The paper also presents an insight to understand the effect of different treatment methods on mix properties and performance parameters of asphalt mixes.The review revealed that the shape properties of aggregates can be enhanced by the incorporating suitable crushing process(two-stage or three-stage).Whereas,physical and durability properties of aggregates can be improved by various treatment methods like polymer coating,Zycosoil treatment.It was further inferred from the review that treatment methods can have moderate effects on the mechanical properties of aggregates,since,it is mostly dependent on properties of source rocks.
基金The research work described herein was funded by the National Basic Research Program of China(973 Program:2015CB057806).This financial support is gratefully acknowledged.
文摘This paper presents an analysis of a tunnel failure accident during shield tunnel construction on Foshan Metro Line 2 in China.The failure is caused by the leakage of the multilayer seal system,which consists of several brush seals at the tail of the shield.Four different failure modes for the multilayer seal system are discussed.A simple structural analysis of the brush seals is then conducted,and failure mode 4(failure due to brush seal deformation)is identified as a major reason for the Foshan tunnel accident.A finite element method(FEM)analysis is employed to validate the conclusions drawn from the simple structural analysis of the brush seals.
基金This work was supported by the National Key Research and Development Program of China(No.2018YFC0807600)the National Natural Science Foundation of China(No.51776192),the Youth Innovation Promotion Association CAS(No.CX2320007001)the Fundamental Research Funds for the Central Universities(No.WK2320000048).
文摘Ceiling gas temperature rise is an important evaluation indicator determining the level of risk in a subway tunnel fire.However,very little literature has been found that has addressed the emergency when a fired subway train with lateral multiple openings stops in the interval tunnel.Hence,a battery of full-scale numerical simulations were employed to address the impact of train fire location on the gas temperature beneath the train ceiling.Numerical results showed that the ceiling gas temperature rise is affected by the pressure difference on both sides of fire source and the backflow from the end wall,which depends on the heat release rate and the fire location.The ceiling gas temperature rise decays exponentially in the process of longitudinal spread,and it can be predicted by a dimensionless model with a sum of two exponential equations.Finally,based on a critical fire location(L'cr=0.667),two exponential equations were developed to quantitatively express the influences of the fire size and the fire location on the maximum ceiling gas temperature.The research results can be utilized for providing an initial understanding of the smoke propagation in a subway train fire.
